{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>week</th>\n",
       "      <th>temp_2</th>\n",
       "      <th>temp_1</th>\n",
       "      <th>average</th>\n",
       "      <th>actual</th>\n",
       "      <th>friend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fri</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45.6</td>\n",
       "      <td>45</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Sat</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>45.7</td>\n",
       "      <td>44</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sun</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>45.8</td>\n",
       "      <td>41</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Mon</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>45.9</td>\n",
       "      <td>40</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Tues</td>\n",
       "      <td>41</td>\n",
       "      <td>40</td>\n",
       "      <td>46.0</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  week  temp_2  temp_1  average  actual  friend\n",
       "0  2016      1    1   Fri      45      45     45.6      45      29\n",
       "1  2016      1    2   Sat      44      45     45.7      44      61\n",
       "2  2016      1    3   Sun      45      44     45.8      41      56\n",
       "3  2016      1    4   Mon      44      41     45.9      40      53\n",
       "4  2016      1    5  Tues      41      40     46.0      44      41"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('D:\\\\Py_dataset\\\\temps.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征说明\n",
    "\n",
    " * year：年份\n",
    " * month：月份\n",
    " * day：天\n",
    " * temp_2：前天的最高温度\n",
    " * temp_1：昨天的最高温度\n",
    " * average：在历史中，每年这天的平均最高温度\n",
    " * actual：实际温度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(348, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据规模\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 348 entries, 0 to 347\n",
      "Data columns (total 9 columns):\n",
      "year       348 non-null int64\n",
      "month      348 non-null int64\n",
      "day        348 non-null int64\n",
      "week       348 non-null object\n",
      "temp_2     348 non-null int64\n",
      "temp_1     348 non-null int64\n",
      "average    348 non-null float64\n",
      "actual     348 non-null int64\n",
      "friend     348 non-null int64\n",
      "dtypes: float64(1), int64(7), object(1)\n",
      "memory usage: 24.5+ KB\n"
     ]
    }
   ],
   "source": [
    "#该数据集的基本信息\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>temp_2</th>\n",
       "      <th>temp_1</th>\n",
       "      <th>average</th>\n",
       "      <th>actual</th>\n",
       "      <th>friend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>348.0</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>348.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.477011</td>\n",
       "      <td>15.514368</td>\n",
       "      <td>62.511494</td>\n",
       "      <td>62.560345</td>\n",
       "      <td>59.760632</td>\n",
       "      <td>62.543103</td>\n",
       "      <td>60.034483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.498380</td>\n",
       "      <td>8.772982</td>\n",
       "      <td>11.813019</td>\n",
       "      <td>11.767406</td>\n",
       "      <td>10.527306</td>\n",
       "      <td>11.794146</td>\n",
       "      <td>15.626179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>45.100000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>49.975000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>47.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>58.200000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>69.025000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>77.400000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         year       month         day      temp_2      temp_1     average  \\\n",
       "count   348.0  348.000000  348.000000  348.000000  348.000000  348.000000   \n",
       "mean   2016.0    6.477011   15.514368   62.511494   62.560345   59.760632   \n",
       "std       0.0    3.498380    8.772982   11.813019   11.767406   10.527306   \n",
       "min    2016.0    1.000000    1.000000   35.000000   35.000000   45.100000   \n",
       "25%    2016.0    3.000000    8.000000   54.000000   54.000000   49.975000   \n",
       "50%    2016.0    6.000000   15.000000   62.500000   62.500000   58.200000   \n",
       "75%    2016.0   10.000000   23.000000   71.000000   71.000000   69.025000   \n",
       "max    2016.0   12.000000   31.000000   92.000000   92.000000   77.400000   \n",
       "\n",
       "           actual      friend  \n",
       "count  348.000000  348.000000  \n",
       "mean    62.543103   60.034483  \n",
       "std     11.794146   15.626179  \n",
       "min     35.000000   28.000000  \n",
       "25%     54.000000   47.750000  \n",
       "50%     62.500000   60.000000  \n",
       "75%     71.000000   71.000000  \n",
       "max     92.000000   95.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#该数据集的统计特征\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.数据预处理**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.datetime(2016, 1, 1, 0, 0),\n",
       " datetime.datetime(2016, 1, 2, 0, 0),\n",
       " datetime.datetime(2016, 1, 3, 0, 0),\n",
       " datetime.datetime(2016, 1, 4, 0, 0),\n",
       " datetime.datetime(2016, 1, 5, 0, 0)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 时间特征预处理\n",
    "\n",
    "import datetime\n",
    "\n",
    "years = df['year']\n",
    "months = df['month']\n",
    "days = df['day']\n",
    "\n",
    "#datetime格式\n",
    "dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year,month,day in zip(years,months,days)]\n",
    "\n",
    "dates = [datetime.datetime.strptime(date,'%Y-%m-%d') for date in dates]\n",
    "dates[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>temp_2</th>\n",
       "      <th>temp_1</th>\n",
       "      <th>average</th>\n",
       "      <th>actual</th>\n",
       "      <th>friend</th>\n",
       "      <th>week_Fri</th>\n",
       "      <th>week_Mon</th>\n",
       "      <th>week_Sat</th>\n",
       "      <th>week_Sun</th>\n",
       "      <th>week_Thurs</th>\n",
       "      <th>week_Tues</th>\n",
       "      <th>week_Wed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45.6</td>\n",
       "      <td>45</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>45.7</td>\n",
       "      <td>44</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>45.8</td>\n",
       "      <td>41</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>45.9</td>\n",
       "      <td>40</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>41</td>\n",
       "      <td>40</td>\n",
       "      <td>46.0</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  temp_2  temp_1  average  actual  friend  week_Fri  \\\n",
       "0  2016      1    1      45      45     45.6      45      29         1   \n",
       "1  2016      1    2      44      45     45.7      44      61         0   \n",
       "2  2016      1    3      45      44     45.8      41      56         0   \n",
       "3  2016      1    4      44      41     45.9      40      53         0   \n",
       "4  2016      1    5      41      40     46.0      44      41         0   \n",
       "\n",
       "   week_Mon  week_Sat  week_Sun  week_Thurs  week_Tues  week_Wed  \n",
       "0         0         0         0           0          0         0  \n",
       "1         0         1         0           0          0         0  \n",
       "2         0         0         1           0          0         0  \n",
       "3         1         0         0           0          0         0  \n",
       "4         0         0         0           0          1         0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.get_dummies(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "总体来说，该数据集比较干净。其数据预处理工作进行了两项任务，时间（日期）数据的标准化处理，对类别数据进行了one_hot_encoding编码。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.特征展示**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\taon1\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_converter.py:129: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Friend estimate Temp')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kJEycONHm9v/88w+2bt2KLl26YN26daKSWEajUTaJCQA2b96MNWvWIDIyEpmZmXjttddcWlfRJCRffPGFy41je8glY5mWV/VajgThLlQqlWSZ6X7q1asXfvrpJ+5jtW/fHs2aNcOff/6J9PR01KpVCz/99BOCgoIwaNAgu/ubzit3v6empoq2A2COiZXLEM/NzZXEkAYEBGDq1KmYOnUqUlJScPjwYfzyyy/YsmUL/v33X/z111+ScoU8jBo1Cq+++iomTpwIf39/u0b6/PnzkZWVhfnz5+OZZ54RrVu3bh2zUoCJ8ePHIzs7G5GRkVi3bh2GDBni0tjNsLAwpKamIjU11aWeXHvo9XpkZ2dLjNasrCzo9XqvfBZQDCvhFMLCwjBo0CAkJSUhMDAQw4YNs7n9tWvXAJRle1oL4okTJyTTQSYSEhLwyiuvIDw8HLt370aPHj2wadMmLFmyxDlfhMH9998PAMzpn8rGlDRiSUlJCU6ePAm1Wk2NDgjCiTRt2hTh4eE4ceKEJB7VHk899RT0ej1++eUX7N+/H7du3UK/fv0QGhpqd1/T1PHRo0eZ5zWVxLJM6jLVn71165Zk+6tXr9r1uNWpUweDBw/GTz/9hA4dOiA+Ph6XLl2yO1YWQ4YMQWhoKJKSktC/f3+7+QWm5wErUYqleSbmz5+P33//Hf369cMff/yB0NBQvPTSS8xr4CxMz4MjR4647By8sMqbmZaxwg+qOmSwEk5j+vTpWLVqFdavX28zGxSAObbH+oZLT0/HlClTmPvodDo8//zzyM3NNZcsWbhwIerWrYu3337bZTE7Dz30EO677z6sX79eNsnh33//lY25dSbbt2/HgQMHRMu++OILpKeno0+fPtQ+kCCciI+PD1588UWzLhUVFUm2yczMZGrPiBEjoNFosGbNGnOylbX3UI569erhscceQ1JSEr788kvRuosXL2Lp0qXw9/c3J3ACZZUP1Go1fvnlF9F0dGFhIaZOnSo5R0ZGBv7++2/Jcq1Wi9zcXABwuLB/cHAw1q1bh1WrVonKTslheh5Ya9uuXbtky3edPHkS77//Pho0aIBvvvkGd911F7744gtkZ2dj7NixLuum9eKLL0Kj0eDNN980J5ZZotPpmIakK5g1a5bob52fn4/Zs2cD4P+tVSUoJIBwGvXq1ROVHbFFu3bt8OCDD2LLli3o0aMHHnzwQaSlpeHPP/9EXFwc6tSpI9nnvffew4kTJ0QlSyIjI7FkyRI88cQTGDNmDPbu3VuhhAEWKpUKy5Ytw8CBA/HCCy/gm2++Qfv27REeHo6UlBScO3cOFy5cwMaNG5njdia9e/fG4MGDMXDgQDRo0AAnTpzAvn37EB0dzUwIIwiiYkydOhUXLlzAihUr8Mcff6Bz586oV68eMjIycP36dRw5cgRjx46VeLRiYmLw2GOP4Y8//sDly5e5aq9aMnfuXPTq1Qsff/wx9u/fj/vvvx+3b9/Gxo0bUVxcjC+//FJU0io6OhrPPPMMVq5ciUceeQQ9evRASUkJdu3ahdjYWIk2JScno3v37oiLi0Pbtm1Rr149FBYWYvfu3bh69SqeeOIJcy1aR1CSCPv8889j9erVGD16NPr37486derg4sWL+PPPPzFo0CBs2LBBtH1ubq65QszSpUvNL+pDhgzB/v37sXz5cvzvf//Du+++6/D45WjdujXmzZuH119/HR06dMBjjz2Gu+66CzqdDrdu3cKRI0cQEBCACxcuOP3clgQFBaF27dp48MEH8cQTTwAAtmzZglu3bjEr2ngD5GEl3IJGo8GPP/6I559/HikpKVi0aBGOHDmCkSNHYv369ZKagL///jsWLFiAVq1aSUqWdOrUCW+99RauXLkiKQHjLBo1aoT9+/fj7bffhlqtxtq1a81jrlu3Lj777DO7SWbOYMSIEfj+++9x5coVLFy4EGfOnMHQoUOxc+dONGjQwOXnJ4jqho+PD1asWIElS5agefPm2LlzJ7755hv88ccfKCkpwaRJkzBu3DjmviYvl06n46q9aknDhg2xd+9ejBs3DgkJCfj666/NGf2bN2/GyJEjJfvMnTsXkydPhk6nw9KlS7Fr1y4MGzaMqamxsbGYPn06YmJicOjQISxYsAAbN25ErVq18NVXX2HZsmUKrlLFaNmyJbZs2YIOHTrgjz/+wNKlS5Gfn4+VK1dKygICZeWhEhMTMWPGDPMUvYnZs2ejRYsWmDdvHrM0ojMYOXIk9uzZgyFDhuDcuXNYvHgxfv75Z1y7dg19+/aVrT3uTFQqFX788UcMGjQIW7Zswffffw8/Pz+8//77WLhwocvP7w5UOTk5grsHQRCEbd555x188803WL58OQYMGODu4RAEQRBuokmTJtBqtUhKSnL3UCoV8rASBEEQBEEQHg0ZrARBEARBEIRHQwYrQRAEQRAE4dFQDCtBEARBEATh0ZCHlSAIgiAIgvBoyGAlCIIgCIIgPBoyWAmCIAiCIAiPplobrPHx8e4eAjc0VtdQlcYK0HhdSVUaqydTla4jjdU1VKWxAlVrvFVprIBzx1utDVaCIAiCIAjC8yGDlSAIgiAIgvBoyGAlCIIgCIIgPBoyWAmCIAiCIAiPhgxWokoiCNTvgiAIwpmQrhKeDBmsRJXit8RixK5KRoNVKdhwrcjdwyEIgqjypBUb8PjWNNT4IRnP782C3kiGK+F5kMFKVCneOJKLPJ2AAr2AVw/lwEgeAYIgiAqx7FIhjqfrAADrrxdj560SN4+IIKSQwUpUGQRBQFKRwfy5QC+gSE8GK0EQREWY9U++6PPHVp8JwhMgg5WoMrBmqUoNZLASBEE4EwOFBBAeCBmsRJVBZ5QuKzFIlxEEQRCOQxNXhCfiVoM1Pz8f06ZNQ8uWLRETE4MePXrg5MmT5vWCIGDWrFlo1qwZYmJi0LdvX1y8eNGNIybciY4Rr1pCHlaCEEG6SlQUSroiPBG3Gqyvvvoqdu/ejYULF+Kvv/5C165dMXDgQCQnJwMAvvzyS8yfPx9z5szB7t27ERUVhUGDBiE/n+JrqiMGpoeVhJUgLCFdJSoKeVgJT8RtBmtxcTE2b96M9957D4888giaNGmCt956C40bN8bSpUshCAIWLlyIiRMnYsCAAWjRogUWLlyIgoICrFu3zl3DJtyIjvHWX1IFlXXHzRK0WXsbHTak4miq1t3DIbwI0lXCGRgZzgFPJ63YgME7MtD0pxR8fDKPasp6IW4zWPV6PQwGAwICAkTLAwMDcfjwYSQmJiI1NRXdunUTrevUqROOHj1a2cMlPABWDGtxFfOwGowCJv2VjcQCAy7n6vHm0Vx3D4nwIkhXCWfACr/ydL67UIjdyVqkFRvx6el8nM/Wu3tIhJPxcdeJQ0ND0aFDB3z22Wdo3rw5oqOjsW7dOhw7dgxNmjRBamoqACAqKkq0X1RUFFJSUmSPGx8fr2gcSrd3J9V9rCklKgCBomXXbtxC7fyKuQMq87qmlKiQXFT+HU5l6rz6NwtUrfHyjjUuLs7FI3EM0lXl0FgBIEj0SaszVPhclX1dPzsj/g6fHknGO3Gl3PvT78B1OEtX3WawAsCiRYswYcIEtGjRAhqNBm3atMHQoUNx+vRp8zYqlUq0jyAIkmWWKHmQxMfHe+yDxxp3j9UoCPj9Rgk0aqBn/QCbfwNXjVWTpweOp4qW1Yyui7iGgTJ72Keyr6shRwccTxMtu/vuu21eT0vc/TtQSlUab1Uaqy1IV/nxhLGez9LhfLYOj9XzR2SARnY7l471YJL4s1pdoXO55bpafYeQsDDExdXg2tUTfge8VKWxAs4dr1uTrho3boxt27YhKSkJ58+fx+7du6HT6dCwYUNER0cDANLSxA/3jIwMiXeAcD0T/8rBM7uz8OSfWZjmpmlsVuaqtoqFBOSVSr3BrFAHgnAU0tWqw4EULR7dkob/25+NhzamId9DxICV4FrVCNLwOQGIqoNH1GENDg5GTEwMcnJysGvXLvTp08csrnv27DFvV1JSgsOHD+OBBx5w42irHzqjgBWXi8yfF10sdNM4pMuqWgxrjpZKcxGVA+mq5zPxr2yzrt0uNmKxm7TVGn0VjGG1JtCHDFZvw60hAbt27YLRaERcXByuX7+OGTNmIC4uDs888wxUKhXGjx+Pzz//HHFxcbj77rvx2WefITg4GEOHDnXnsKsdrG5SRkGAmnMa21mwqgRoq1jjgByGh7WUah4SToR0tepwNU8sYPuStXi9daibRlOOvop5WFkVAQLIYPU63Gqw5uXl4YMPPkBycjJq1KiB/v3745133oGvry8A4LXXXkNxcTGmTp2KnJwctG/fHhs2bEBoqPtv6OoEywGoMwL+8uFWlTYOT/Gw5miNmHY0Bxey9Xi+WTBG3RMsu501Vc3oJjwb0tWqi2eomWfVYV15uRDfXSxE8xo++OSBCET4SyeGWc8BigjwPtxqsA4aNAiDBg2SXa9SqfDWW2/hrbfeqsRREdawbEKtQYB/JSsC28PqGcq68EIBfrpaDAB47a8cdKnrj0ah0tuL5WH1lO9AeAekq1UXgxdMxTuTxHw9XjmUAwA4m6VDwxAfvN0uTLIdK9SKNTNIVG08IoaV8GxYhiJrmevHIV1W7CGugDmnxF2Cvv+XHYtGBitBEHK4KzrIU2fPvzxbIPr86Wl2Nza2rrpkSIQbIYOVsAsrnklODARBwJk8Nc5l6Zw+Dpb3wVONPTmDnuUJ8NTvQBBE5WLLwZpcosKBFK1L9MLHQy2B1GI+q5MZakW5AV6Hh/5MCU+ClTEqlyg08a8cPH8mAA9vSsOiCwXMbRylKlUJCJF5AlDSFUEQcshJwb5kLYafDMAT2zPQa1s6DE7WDJ9KTqDlpZBzBo1mrqoHZLASdmHV5GPFB2WWGLDcovyVs9uOVqU6rCG+7AdALkNYS2jqiiAIAEaZtKuXD2VDayzTlH8ydPjtRolTz6tmWAKszPvKpoCzLm1uadV5NhCOQwYrYRfW9DbD7sKtQtdaXiztKvGAGFaWsMsZrKzxUnIAQRAAO8EVAG4WiLX1wG2tc08sUwnG3RTo+LSRravOHg3hbshgJezCsglZRqyrX8hZHlZPKLpfwLhAcjVqGaFW5AkgCAIAf9KVsyfwWRrvEdrKabCy4lU9YfyEcyGDlbAL71S8q+XBU0WVFfDPumYA25tKBitBEAD/S7/ayRYrywHhCdrKGxLA0lXKDfA+yGAl7MLSLVZIgKv1gSWq1lNl7iCHET8lF6nAegiwvK4EQVQ/eOuwOtNgFQSBOf3vCdrKHRJAjoBqARmshF2YMazu8LAyRPVSrh6FCoKtdEYBl3N0yGNZ3A5QpDfiWJo0nkyJh5ViWAmCAPg9rConBgXIORpOZZYqOs6tAj1uFeidMKIyrufpJS/+fjIWCytelQxW78Otna6IqgHLUGRNtxgZaisIAlROKpnCKq9lFMo6oDwY7W93f61BQL/f0/F3ug51gtTY3KtWhcZzu8iAvr+nS/qBl41VZgxVqNIBQRCVC+9rtDMjAuTe909l8NfSXni+ANOPlVWFmdkhHD38KjamN4/kYNFFafOVYJlkVrauVmwMhOdBHlbCLuykK8Z2Lg4TkBPWC9l8b/Ubrhfj7/QyEU4pMuJ/J9ldU3hZcL6AaawC8h5WloiSwUoQBMCvl84MCdDJuHXPZ/MZrIIg4K1juRBQNsv29rHcCiXgphQZmMYqAATL1LdmhQRQDKv3QQYrYRdWkWqWkcUyKJ1pi8l1jypiWcoMVl4Wi+CvCcUVGs9X5+QbI8h6WCmGlSAIGdxhsLLqbAP8ba9Zus8I6+fmbKa8oSxnCLPCqjwhaYxwLmSwEnZhChJDWVkGJWsa31HkhJW7FEwlNnNheVj1RoFpwO+85dwi4ARBVE1YYVUsnOphlRFQXnuPNR1fkRQBW6eVe56wHAEJ+QandwQj3AsZrIRdmK1ZGVPbTIPVid7DigprZTYfZH1vuan/o2mlSMh3XrICQRBVE0+KYeWtWMAs1VcB3bdltMs9T+TiVd8/kef4QAiPgwxWwi68SVeuDgmQm6HySIOVZeTbEPG1V4vkVxIEUS3gbxzgPDWT81ry6iqrtbSpjawj2Ei1QxgAACAASURBVLoGsh5WmZ2+PleAtGLKvvIWyGAl7MJ609YxY1jZ0+DOQu5Y/LULK89kVeJhBYBr+SSqBFHdcUd4k5zXkncszvaw2qpSKDdWW6UBF12QzzUgqhZksBJ2YQkIS5Bcn3TFXu6ZMazSZbaSAGoH0K1IENUdd8SwyjkCeHXV2TGstpJo5TystrR1yb+FTqu7TbgXekoSdmEJGiskgLXMqR7WCk5duT0kwMZA8zk7uhAE4b2w5FJwYuIqi4rGsJYwYrUqEhJQZKM6gbyHVf54uaUCll9il8kiqhZksBJ2YekHy/hiiQlnZRQu5D2sfCdxe9KVjZf8HPIAEES1gqVbvDWvK6NcIO85WI6KioQE2CqnZRDYBrxcDKuJBRcKqN61F0Cdrgi7sMpJ8Za12pOkRVJREXrWD8D9tf2QkK/H6vgi3BPhgyGNAxV1wZKNYeUUR2eEBORojVjybyECfWwfjOVhtSWYOVSMlSCqFSw5YGkoa9ntIgPmnMpD7QANRjYNgkEAll0qRL5OwNhmwYjw5/dFyckSr64ym6EolDNBEPDr9WKcz9YhscB2PL9eAKwbXllra4BGnAyWUmTEL1eL8N+mwcoGRngUZLASdmEJJmsKhmXETjqcAwCYdyYff/aLwsAdGci9U1W6QCfguXv4BURuOqgyQwKe2pWJw6n2e2wrTboiDytBVC94dZXlYV0VX15V5FahHpklRvxwuWzZjpvF2NmvdoXGASgoa8UMD1OmtquvFOHlgzlc2+qNgK+VPW6trYcGROO7iwWijllfnSvAM3FBlZp8SzgXCgkg7MIMCVBYc9UgAF23pJuNVQCY+BefQJmoqLBWNFEhR2vkMlYBmbhf8rASBHEH/nKBtvXt8zMFZmMVAP5O1+FmAX9dZ9lQK8792TGs3KcHAHxjo2ugNTx1wf00wISWIdBYaH58rh6/3aAmLVUZMlgJu7C6hbCML1f3bpaduuI8LWs7JTG2uQq8oKzj2spkzalIL0OCIKocLDkwCFK9dWTyRUlbUrkXfiPneZ0Rw/pvDr+BzVOBJUCjQmyID4Y2CRQt/+JMvsuT2AjXQQYrYReW8fXT1WLJm7+t+nnOQO74vPrDmpJX8jAoUJDJzzTybZwrt9RIQkoQ1Qi5mPyzWTrRZ3seVhZ+CqaT5KsE8O3PjmF13bQ7T9Uavzuu1ddahYqWn8jQ4eBtvlkywvNwm8FqMBgwc+ZMtG7dGtHR0WjdujVmzpwJvb78TUsQBMyaNQvNmjVDTEwM+vbti4sXL7pryNUWOcHcl6wVfXZmCSsWch5c7mzWCiYHFCiwyJm1a20M1CCUtWgliIpAulp1kJOTmSfF7UQd0VUle8iFKvGGWlXUEQAAwXaSWC1hOVCsx+B/x2BvUcMXPev7i9Z9fiZf2eAIj8FtBusXX3yBJUuWYM6cOTh27Bhmz56NxYsXY+7cueZtvvzyS8yfPx9z5szB7t27ERUVhUGDBiE/n35wlYnctHmiVZyUq0MCruSyp424hZU5dcUvlAUK4gd4RNWa8QeyFRnFBGEN6WrVQa6udKbVW7QjIQFKpPhKnpyu8u3P0jWlIQGh1mn/NrA24AVBkFwjf035vye2FntZ9yZrcSRV7GwhqgZuM1iPHTuGXr16oXfv3mjYsCH69OmD3r1748SJEwDKfoQLFy7ExIkTMWDAALRo0QILFy5EQUEB1q1b565hV0tY09uA1GPpSlsrX2dEvKzByneMirQQzCs1Yr6SxACjgCu5Onx3oQBnMss8p9bC3jBEI/p8Pd+A6cdyuc9BENaQrlYd5MpGWeuUIyEBvC/xAHAqQ8dczm2wVtAR8MfNEtwu5n94FOkFrIkvxLprRTAYBUlIgp8aonKJHaP98XCMn2ibOafo5awq4jaD9cEHH8TBgwdx+fJlAMC///6LAwcOoHv37gCAxMREpKamolu3buZ9AgMD0alTJxw9etQtY66uyGX/WwupK0MCzmTqZKe5eIWVlYjAir+yRhAE9NqWjt3J/G/lifkGPLo5HW8czUWXzek4nl4qMY57xwZgXDNxWa8Vl4vwW2Ix93kIwhLS1aqDnIfV+sXfVvUVOZTUyD+VKR+KxNOUpSKhVvPPF2D4n5l8G99h5O4svHQwB2P3ZeONo7kSg9lfIzWW32wbJvq8h7ysVRK31WGdOHEiCgoK8MADD0Cj0UCv12PKlCkYO3YsACA1NRUAEBUVJdovKioKKSkpsseNj49XNA6l27sTd401PcsXgK9keUp6BuLjb5s/Z2T7QelPivc7HbqtAeDPXJeTm4f4+Ay7xyjUBsD6Ha1UUNkdw/EcNS5kB3CN00RSUbmKCwDG7U5F7yg9gPI3/aK8HIxroMPOwAAkFJePa8L+TNRsV4xaYqeAmar0mwWq1nh5xxoXF+fikTgG6apy3DXWK4UqAIGS5YXaUtGYruWqASjTn+sJidAE2zc29QJwPT9Idv2l+CuwF16anC59PpQa+a7r28fkzy07JouZtu//LcTA0AxYXkcfGCXnjgHQPtwfJ3LLZ7Xe+ysV37QsN1rpN+s6nKWrbjNYN2zYgJ9++glLlixBs2bNcPbsWUybNg2xsbEYOXKkeTvrTkiCINjsjqTkQRIfH++xDx5r3DnWkMwcIFnaizm0RiTi4srfXANTsoA0Zd5B3u8UrisArrCny0NCQxEXV9PuMYzHU2BdXVBrsD+Gvy4VAlBWM9aa60VqBITXBFAeVtAgqiZaNQvDD7VK8fjWdHPca45ehXnJNfDz45GS33pV+s0CVWu8VWmscpCuKsOdYy3OLAX+SZeuUPsiLq6B+WNKihY4a/+F3JJ6DWIRFynzxmtBoc4IHJJ/UWly191Mj6UlITm5wE1xuJTWqOK7rgeT7G9jh6yQegCyzJ/D/H2Y534/RIsntpdfx6M5GmSFxeKBaH/6zboQZ47XbSEB7777Ll5++WUMGTIE9957L5588klMmDAB8+bNAwBER0cDANLS0kT7ZWRkSLwDhGuRm17SWcdacUyvK6VEL2DioWy8eVQ+trNCyQEc+zqrMUpGidhYDrnTrqVtLT9Mu088ZfXHLS2WXSqCUj47nY+2627juT1Z1IygGkK6WnWQ0y3r5FVHYlh5dtmcUIx261NtbsMTC8uOYbV/fmdxMEU8tR8ik8D1SB3HY1mPp5eiy+Y0PLQxFYduUyiBu3CbwVpUVASNRpx0otFoYLxTrbhhw4aIjo7Gnj17zOtLSkpw+PBhPPDAA5U61uqOXGyqdWamI8Jqj82JxaIuLiwqUn5Fa6i8Nn1/W5WtCrYQ1omtQvBAbbGYzjyZp+iansksxcyTeUjIN2BjQjG+/1fqFSe8G9LVqoNcbKozDFZ7L/ElegETDmYj1U6yE48zwBlVAiqCtQEZYt231QLrWNbdyVpcyWUnnVny+l85OJ2pw/lsPV45mE01s92E2wzWXr164YsvvsCOHTuQmJiILVu2YP78+ejXrx+Asimr8ePH44svvsDmzZtx4cIFvPTSSwgODsbQoUPdNexqiVz2v0RYXXAPv3ww2+42ctm2lrBKnwB8wuoskzYhX+yCtizl4qNWYVHnGqJ6hFlaI05n2hdTE7P+EXsLPrKq50h4P6SrVQc5Q9Rabx2pvmLvJf5omhb5HILNo62V3TjAmutWuirnYQXKvKz/iRLH216002XLKAg4Y9HM4Vq+AYVKWiQSTsPhGNbCwkIEBgZCrXbM5v3kk0/w8ccfY/LkycjIyEB0dDRGjRqFN954w7zNa6+9huLiYkydOhU5OTlo3749NmzYgNDQUBtHJpyNbDarJCTA+TcxTw1CPi8Ae3lJJXoCiq0Gau0JaBTqg54NArDhenkc8MEULf4TZT8WDQDyqIZrlYd0tfogZ/NIylo5oKv2d+EzKLmqBDAM7xIXhIfJIdVV29+tRQ1fHE8vN0BvFxnQzIYlxDJOHancQFQcRap49uxZjBgxAvXq1UNsbCwOHDgAAMjMzMR///tfHDp0iPtYoaGhmD17Ns6dO4fbt2/j9OnTePfddxEQUJ4NqVKp8NZbb+HSpUtITU3Ftm3b0KJFCyVDJpyAbL3ASggJ4IFHVFlxVgBQwuEJ4OlyOKyJNNvXHixhfThGXAnhoIJ4KbmONYRnQ7paPZGrb60XxJrmyMyVPSnw09heb4LHLithGHQ8uspDrQA1utRhV4eRw1ZIAADEBIm/fGqR7W/Jasnt6iY5BBtug/XkyZPo0aMHzp07h379+oliOCIjI5Geno7ly5e7ZJCEe5GvF2hVh9UF97Ad7QHA52GVM+aKeeqw2t8EYX7KPWIsYbVOCjiSWsr9IuBIRxzCvZCuVl9s6aXlZIlDMax2tIA3BJMnJMBRDytPHGiQjwo+CqU1xE4drphAscGaYuchUMiYuSLngHvg/il8+OGHaNiwIY4dO4b//e9/kh9b586d8ffffzt9gIT74Y1hVXoT87yD+3G4N3n0nNU0AODzBPBM/4QpaC1ogiWsceE+qB1YflsW6AXuOFZXhGQQroV0tfpiyxC11FZHqq/Ym3Xi9RA6Gm7FE2rFc+wyg1WZttoLCYgJEps9t4tsX2C2h1XRkAgnwW2wHj9+HP/9738RHBzMrNdXv359c1FqouqRXmzAmL1Z6LMtHbuSSkTrXNWa1Z9jWsp5Hlb2cjlPQI7WiAkHsxGxLAkT/7JfgzXUIQ+r9D5SqVTSsIAU22EBu5NK0Pf3dFywkzwAAEmFBozek4V+v6djv53jEq6HdNW7EQQBX5/LR4+t6fjgeK6o4oqtF2HLl09XVAng6fBXdhwHY1htOAJWXC5E3I8pqLU82e6xA31UdhsXWGM3JMDKwypnsKYVGzBuXxYe3SKtlWv9nQVBwBdn8vH41jTMPJHn0q6P1RlFT1lfX2m3IxNpaWnw91cWa0J4Du8ez8OG68X4K7UUo/eKa3jKTV1ZC6lSYfWzU5CadxtHawWWLWdv//mZfKyO56+DGuqIh1VGWJXEsRbojBi5OwuHbsu3V7Tk7WO5+DWhGAdvl+LZ3ZnMkjRE5UK66r0cSi3FjL/zcCy9FPPOFuCnq+WaYku3LD14jsRL2rutnelhZc1eyTkC/s3R4dVDOUjnzHYN1DgQEmDXw2ptsLLH8tGJPKy9xm6EYz2buCtJi/dP5OF4ug6fncnHpgRqr+0KuH8KrVu3xs6dO5nr9Ho91q9fj/vvv99pAyMqlx+vlAtpXqmAny2EVT4kQPxZqYfVh6MiP09IgKO1AgF5T8DX5wqYy+VQGsOqUQEBMh5mJXGsWxJLUMAZPGwUBGy0ENK8UgEXs/nLZhHOh3TVu1lj9dL78sHy2RpbelnK6YmVw95LPG/4Fo9dy9LWUkHFnJn79rwyXQ32UXE9Jyyx52GtHagWhaNlao3Mv8VKGw4L62ff2H1Zos/fXaQa2K6A+yk7adIk7Nq1C1OmTMHly5cBAFlZWThw4AAGDRqE+Ph4TJo0yWUDJSqXExnlHjv5eoEV87DyeEZ5QgLkTqs1CDieXoq0YoOswbonU4O/00phMAq4lKPDvzmOGXBKY1iDfVWyrTCVxLFmKqgfcyVXGjLgrC5ehGOQrno3eTaCHW15OS3jVl0TEsD/kssiPleHC3deduXCrbYkliBHa0RmiQEn0ktRohfsNiqwJtCBpKtgOzEEPmoVogLFB80sVSaE1gZ/Tqn4c1JhJdb1qkZw12Ht3r07vvrqK0yfPh1Lly4FADz//PMAgODgYCxYsAAdO3Z0zSiJSue8RaFkubdxSb1AhcLKs7kvl4eV8YZvENDjt3ScztQhzE+FKa3ZNSaLDCp0/00co/ROuzDmtrYI5rGsLQi1ocKmOFaeeqxK6h3+wzB6qXSreyFd9W6aRvgAN8TLskoMqBmgsZlMZWnMOpLgY09beY/Jkv5vzuXjnb/LmpJMaR0qm9D63F6x17FFhA9iQ5WVfg90IOmKJzwrOlCDNAvjOUMn3sdeBQOxB1y67b01HC5xT9hA0VV99tln0b9/f+zcuRNXr16F0WhE48aN0bNnT0RERLhqjIQbiLfwxsmJW0VDAnhe8nnerlnH+e1GsdkrmVcq4N3j/F2fZjrQISr4TnIAb2kve3FWEoP1thYTGUZ3kYL5Qtb0P5VncT+kq96LmlEL5UKOHg/HaGx6WO0ZRPaQS5Q1wethZW1mMlYB4LMz+Qjz4zMoL+TouRJDLQnyUXE5NiyxFxIAAHWC1DhrYU+na8XfwboZgTWWXuWEfOl3qikX70VUCC6DtaSkBIsWLULbtm3RpUsXDBkyxNXjItyMpeHFGxKgNDmAJySA55CsbTZWctB7oI8KIb4qydSQHPYM1oc441iLFBS/ZXVsoQLY7oN01fth3V/Fd+5DmyEBoqQr5ee1m3TloMHK8jzmcWqeIwT6qKBS2BzbnrYC0sQraw9rtpbfw8rSVXc10fF2uOYxAwICMGvWLCQmJrp6PISHYHn78oYEKE0O4OtTbf/GZ20SwFFdwJkE+agQ4c8fFmDPC9A03AdRAfbjWFldZiyxfPtn1WmViz8jXA/pqvfDMlxMy2wmXbm4rBV3SIAkT0HxUCpEkI8aNRToKsBnsEZblbbKsIphzZErH3OHNVdsJyXTzJVr4P4ltGjRgoS1msIfEqDUw8pxbg6Dysg4r1sMVgWVAoLtiCpvPdYiOxfx6T8zzf9m/R3lyn0RlQPpqnfDMmZMy2wZNTpRSIDy89qbveK97603k4tXdRVluurcKgEAUMfKw2odEpBjx6LfdqMEJ9PLEpNZf0c79i7hINxP2BkzZmDZsmXYt2+fK8dDeCA8IQGCICieuhJgP7idR1g9wcMaqNjDan98D0aLwwLi86SxUvl2puMu5OjNhbGZ3h7yBLgV0lXvhjXtX+5hdWMdVt4qAVafK9tgDdQo01WAT1vrWHW7SrY2WDkszg/v5DqQrlYe3ElX33//PWrWrIlBgwahcePGaNy4MQICAkTbqFQqrFy50umDJFwLK0DfcomcYFoud/T+NAiw2cmER1g9wWANVuhhDXXAC5DJKLZtzxMAlHtJmFNX5AlwK6Sr3o1ND6sLQwLs7cKddGV1oMo2WEN8lemqv4avskxcuNj0uV4kPgePrl6/40Bg6yoZrK6A22A9duwYVCoVoqKiUFBQgLNnz0q2kasrSXg2rBvOKJTV4FOrVLLlV8S1Ah07t0Gw/SPkufFZ01+VLRdqlQqBCnoIhnBsWytALKJZDIM1l8MTYLqGrGtJwupeSFe9G1sxrLZDAsr/7ZLGAQ6WtbIXM+9sQn3VymauOIu2Ngr1gZ+6/Dpk6VTI0RrN5+JJnjV1YWTqKuUGuARug9VU1JrwPuSMFp2x7I1Vbr3ldL2jhk+ZsLIfyIIgcPW8Zum+vbIkrkBJz2t7MawAEGllsGYwiq7yeAJMD0Z20hUZrO6EdNW7Yd1fJgPU1kt+RbXVXkKro2WtKltXyzys/MLKo6tAWfOAu8J8cNGizNalHB0eiC7LG+AJCTCdijyslYfCHhKENyJX509nFGzGpuqM5TGojtQKBGyHEugFPk8p69S8guxMlHRk4UkMsPawZlqJqFEQRMWv5TDtpmNcEgoJIAjXwQ4JsF/WShwSoPy89qsE8CZdiberbF0N9nV+boCJphFif90li9rjacX2PSWmhgYsLzoZrK6B28Oanp5ufyMAUVFRDg+GqBz0RgGLLhbiVoEe45qHIEjGNag32i+ErzMCfhrHDR9bnoCKtA+szKkr/zuhpkp6XvMIa4RfWdlx0zfJLRVE04OZJUauRgWm68jy9pCH1b2QrnoXf93WYmNCMTrU9sOQxoG2QwI467A6VtbKTkgA55S1xMOqrO5/hQnxVSPcybkBJpqG+wIoMX++bOFtNSWq2sLvju6zdZV7GIQCuA3Wpk2bcsVSZWVl2d2GcC+fnM7HJ6fyAQDrrhVjex/2w1BnFOzeeKVGAX4alcOFkuV6VQOOF7cGnJcc0K6WL05mSOufWuJ/501bo8DDytM+UKNWoaa/WuRZtWwUc5uzL7fJYCVPgOdBuuo9XM/To9/2DBgF4LuLhfDXqJgv8uaQAM7WrI54WJ2VdGV9nMr2sIb6qriSqEwo8bDeY+VhvZxbrvM82upr9rBK15GuugZug/Xzzz+XCKvBYEBiYiLWrl2LunXr4r///a/TB0g4H5OxCgDpJUasvVbE3E5ntC+WpvWOJAYAtqeueGvZudJgrRessW+w3gm+f7xeAL69UMh1XJ6QAKAsjlVksFp0ZOHxAgAWMazMAtdchyBcBOmq97DgQoHIwHthfzbaRPpKtrOVBGlCJwoJcEFZK85jekIMq6u2b2pVKeCSQg+rr62QAJq5cgncBuuYMWNk102ZMgVdu3Z1yoCIyuc8o8c8cMfDasdoNN2Yjidd2T+2/WMwQgKcZIjVD7bfE9pksHar548HavvhaFqp3X2COTO0IgPUQG75Z0cM1hIbfyPyBLgX0lXv4aKVjhbpBZmQANP/bcwuVTgkwPZ6/qQr98awmrL+53WMwKTDOXa3V+I4uTvcRxRydaPAgGK9AF91mSPHHiafA+sZSbkBrsEpSVfh4eEYOXIkvv76a2ccjqhkWPU9gbKb357RaDJ4KlLWyt6xHTmGvRjWRZ1rYFyzYLvHtm7hx8IUw6pWqbC1dy1s7V0La7tHYm33SNl9eD0BkVYJB9mOeFhtZCWTweq5kK5WLcIYsZasGQy92cMqf6yKhgS4KobV3sxV17r+ODKoNt/BOTBp6+hmwTg8sDbWdo/E5l618HCMH3P7a4zmKnIE+ajRIKRc3wUAV/L0SC8x2g2pAMqNJ1txyoRz4faw2sPf3x9JSUnOOhxRicgZrKXGsjqstjB7Cxx887YlrDwlrQDAyBi+PWEN1KjQo0EAFv9rewqfJ0PV36JJga+6vKWqLW8Eb81W60oBOfry/VKdEcNKIQEeDelq1SGM8RLKqp6itDWrI9rqvCoB4s/2HAF+GhWaRfgi0ldAps62xlnWQZXDMlymeQ1fNK9RFmKx8jLbkVCbw8FgyT3hPrhRUC6Cl3N0MITxmUVaG44Ad1SpqQ44xcN6+fJlLF68GHFxcc44HFHJyE1/lIUE8HpYHU26snFsB9sHAvYN1gANX6H/CD81Hq/nb/5s3dLPdCwW/hoV7gqTCmikvxqNQvlE0boWa47FrGMep+vFdC1YSR7kYfVcSFerFqy49HyGgcfVmtWyKYsDtyjrJV58fM7ZK4WdrgI0pv/bP/53nWuKPnep4y+zpZQWNaSxwQAwtrn9WTNLmkaIj3MpV488zguutRlqZb/tOKEcbg9rhw4dmNmsubm5SEtLQ2BgIFatWuXUwRHOh3UTZclkN+mNgL0EZnNCj4P35uen8/FsXJC5YLMlWu6QgPLtskoMWB1fhMu5tqeG/DVAEEf71lA/FT55MAJvHMlBgU7Au+3D0Of3DKtjyR9nceeamH4sF0fuxLW2qOGDWR0iuDNfIwPEBq9lDGsxZ+muz0/no29sgNtjWK/n6bEpoRitI33RrV6A/R2qAaSr3gPLq3arQPqWaLpt+UMClN+jCy4UIDZUg6fvDjLXCxWNldfDeuf/giBgw/VifHo63+b2poopARyusL4NAzCpVQj2JGvRs0EA1CpgX4qWa1wvtgjB1Tw99iZrcauw7Bq/3joEvRso0xVJpYAcPdrXYocbWHM0rRSHbmtlPeB6oby5gCvRGQWsulwEnVHAyKbBCFDSwaaKwW2wtmzZUiKsKpUKERERaNy4MUaMGIFatWo5fYCEc1GSjMQjlBUNCVgZX4TVV4qwo08U7q8tFgruOKs7YxAEAb22Zdg1VoGyqasgDjVRA2gS5oN1PeR/23IeVgBoF+WH7X0dr6Fp7WG1jGEt4jRYEwsM6PFbOvMhVVkhAdlaIzpvTkP+nTebVd1qol/DwMo5uQdDuuo9sO5H1h1q9rByhwQ4NpZXD+XgVIYOcztFSNbzhluZhvjVuQK8dzzP7vamdqWBHB5WX7UK7/0nHO/d+TzzpP3jmwj0UeGbh2twby+HdaWAyzk6FCnI3Or3ewba1mJ7e7UGQVFJLkd5+WA2fr5aDADYk6zFj4/L505UdbgN1qVLlzr1xK1atcLNmzcly3v06IFffvkFALBkyRJ89dVXSE1NRbNmzTBr1ix06tTJqeOobhQruBl1AiBwhwQ4PiajAEw6nIODA8TB+kqTrg6llnIZq0BZDGsgh4c1NkR6izSPELf061aXfypLKZIYVgcMVgC4msd+QlWWh/XbCwVmYxUA/m9/NpL/SwYr6ar3wHs/mmNYbXpYLbavwNTy0kuFTINV7r4P8VGhwOJ7mEICeIxVAGZNtedhHdZEeu+3tSoBVgm2Hu6xCgm4kqcX6ZQ9BAD/yJQ9rMgzUQkmYxUAfr9ZgiK9EUFK2i5WIbi/1eTJk/HPP//Irj916hQmT57MfeI9e/bg0qVL5v/27dsHlUqFgQMHAgA2bNiAadOmYfLkydi/fz86dOiAYcOGMcWY4EeJkaPnKmtV9v+KZkWey5Le9HKB6zPahYk+Cyjzrt5kTL/JERWosdtBpVO0H+4Klxqscx6MMJc0qRukxhiOagOOYl0lwJGQAFtUVr3AU5nSkj8E6ao3wVujlKvTVQXrsNqDpa1x4T7o11A8pa5UHqICy/QqxMf2jm+0DZUs69UgwOzxVAFY9mhNyTbOpoa/GlEW1nWpUVqezFEqI/GKFeJXmV0eKxtug3Xp0qW4cuWK7Ppr165h2bJl3CeuVasWoqOjzf/t3LkToaGhZmGdP38+nn76aYwaNQr33HMPPv30U0RHRzvdI1HdUFL4uaxxgO3tdU7wsMphbUz5qIBNPSMxuU2o5O271dpUjD+QzXVcFYDagWqE+amgYk7albGxJ3sqtnMdf+x9ojaWdKmBQwOjEaygHaBSJElXFg5kpxisDbtIxgAAIABJREFUleQF8M73/YpDuuo98N6PXElXlh7WCobtsIwaa73+6qEI7H4iyjylb+KVQzlosiaF+1wxQWUx97YS7ae0CUVcuHQa3Uetwq4norC0Sw3sfiIKAxpVzgxMU6s4VuuXa0epjNkrZoUCL64B67TnSHZ2Nvz9HZsaFQQBK1euxIgRIxAUFITS0lKcOnUK3bp1E23XrVs3HD161BnDrbYUKpjuKGvN6toqAbawvvEGNwlEl7plHgDrH64p8J6HqEA1fNUqqFUq2ErWtxZvS+6t6YuhTYJQg6PsVUVgxbCaSuUUOsFgrax6gZUxveeNkK5WHXjvx/KyVvLbiJKuKphtzhqWtffvqbuDEOqrZhoEckm5LGLulJUKteFhbRYhL7qhvmoMbhKE+zgTn5zBPVbG82knGawVfdHggZWX4M0ltWzGsB49ehRHjhwxf96xYweSk5Ml2+Xk5OCXX35B8+bNHRrEnj17kJiYaG5BmJmZCYPBgKgocbJKVFQU0tLSbB4rPj5e0bmVbu9OnDHW+Fw1AL5MyptJpjdr+QfmjaQUxGsNuJmqsbkd19isvt/N2+JjagvyER+fCQBQIxBlvlLlRKj15nOF+gQgT88+jqf8NiJ8As31V/WCCltOXUPLUCMKtOxr0CrUgLP5fPUIcwuLXf494+PjUVToB2u58ZTrawnvmCpSaop01bNw1ljzigLA4wPKzS9EfHwWSvTyGpZzZxsAKDU4rnUAcOHyFQRZyIFBAAxCkPmzCgKuX7kClQrIz/MFwE4i4qE04xbiSwSE+cibFlmptxFv8JwC0CFaHwDlBrJcuFLLUAPOceoqAFy+ngBjsGuMR9NvtqzMYZBo3b9XE6Bz0XkdxVm6atNg3bNnD+bMmQOgLHN1/fr1WL9+PXPb2NhYfPzxx1yDsmb58uVo164dWrduLVpunT0rCAKzBIwlSh4k8fHxVabGobPGejOpBDibybVtreiYsgnzf+Wn2iNrxyDuriBEGguBePut82xh/f1q6AuAK+V9SaNqhCMuriyBwOdIMkod9DA2qhGIuLgGAICwUzcgV5bdU34bj97KwsaE8sD6q5raGBQXitIjybDMQw7zVaFvw0DcF+mLN47mMo4kRe0XgLi4WGcP2Yzpdxt+KwvILBat85Tra6Ky9IB01XNw5lj1J1PArgotxi8wCHFxsTAclm8I4RtwZxujAOGg9GVGCQ0aNUFNi/J4RXojcKh8mt9fo0LTpmXXoGZmDnDbdjMVW3Ro1hi1AzUITbkuu02TBnURV99zytrdLRQCCbafXS1r+uLrh6LQdUs693Hr1I9FnAs8xZa/2dtFBuDobdH6GBed11GceY/ZNFjHjx+Pp59+GoIgoG3btpg1axb69Okj2kalUiE4OBg1azoWIJ2eno5t27bhs88+My+LjIyERqORvPVnZGRIvAOEMpQku/AUP3ZpSIDV1IafxcstR4K/LDEW7gbOpiZupWtdf5HBuje5BJNbh0j+ltefrgONWoWfrhRxH9vRcmRKoZCAckhXvRP+KgGmpCv5bZxRfcWE9Yy+dSiCZehTRXQVKK9qEm4jJMBW3Wp3YC/59quHIjCyqfLE2sqoEsCa/leSp1LVsPm4Dg8PR3h4OADg2LFjiImJQWioNLuvIqxevRr+/v4YPHiweZmfnx/atm2LPXv2mJMFgDLPRP/+/Z16/uqGEoNVZxRgL3yKp0SLo1gf08/C6qmIARRt0b7PVqyVp9DFqmzWsbRSSVxZgAbQmIp2K3ggZFdS1hUZrOWQrnonvNqqN5aVi7L1js9TSYAXa6NG4ghQWxqsjt+owT4qcytvW7qqRJ8qgwg7BmuQg4X4sysh+4nlKKq2MayWuGKKRxAErFixAoMHD5YI9oQJE/DCCy+gffv2eOCBB7B06VLcvn0bo0ePdvo4qhNKMsv1RnEXKRampCxWz+yKIvWwWgurY+e0DPqvCgZro1AfNArVICG/zDVSagR2JYk7wli2mfVT0E47rdiI20UGkdfZFVCVADakq94BTwlAEzoF5QKdoasSg9XqmP5q53hYG4byzVx52strhL/tAfHU62ZxJkuHHgo7bymF1QDCGdVjPBVFE6JXr17Fd999h9OnTyM3NxdGq4bFKpVKlExgjwMHDuDatWtYvHixZN3gwYORlZWFTz/9FKmpqWjevDl++eUXxMa6Lt6uOqDUw2pv81KO6S1Hsa5Q4O8kD6tlBmq+TMKVp/FoHX/8kF8+1f9/+8VxxUGacpNQaXeVU5ml6BVUXkLmuwsF+OFyIVrV9MUnD0bYnTIzYRQEzD6Vj60JxXiojj8++k+4eV0FHDdeD+lq1UfJNGyp0b7nlCdsgBdrg9U6e93yBbciutqiRnmyli1HgMcZrC7ysJ7KKBV9PpelwxtHcqAzCph5fzizHbktFpwvwMrLhWji54eFDY0I81PLeFgdGm6VgNtgPXHiBPr372+eVjp69Cg6duyIoqIinD59Gs2aNUOLFi0Unbxz587IyZEPdh47dizGjh2r6JiEbZTVYRVgrzGWuTWrCzyskpAAJ8WwNrbwBFSVl9Gu9QLww2X52FRLD2uBwuCpUxk69GpQZrBeztGZE7YuZOvRNNwXk9vwTVfvT9Hik1NlvcYv5OjRNtIXHe6sU5PFyoR01TtQNnMl2NVLZ+YGWBswEg+rk2JYLQ3WsKpksNopTRjoqMFqVR5r8uEcHE0rM2JfPJCNk0Oi7SY7mjibpcP0Y2W6fBE++P7fQkxqHcp88fHmGFbumbqPPvoItWrVwvHjx7FkyRIAwJtvvom9e/di06ZNSE5OxqhRo1w2UMI5FCowZvQcngBXJl3Z8rA6Kqw96/uLRKJfbfbr6LNxQczl7uKRGD+bhW3yLf6u90cpyxC9YdEh7NPT+aJ1Hyno7z3dqjLBK4fKjSYjs3i59worL6Sr3oGy+ta2a7AC5S/rzkm6Eo/NWlfFuQGOCatGBTx5V7lmhvpA1EHKhK9aWvfU3YT62gkJsDBYuypow51UaBCFdJiMVQC4nm9QVN/2vb/F2vrBiTJdZnlTqdMVyjwBzz33HCIjI6FWl+1myiDv3Lkznn32WXz00UeuGSXhNJT0SWbFWlnPnpiml0pc8FZnyxOgVFg1qjLP6oz24aLlnWoYMKCROM6oWYQPXm/t3CSYilIzQIM2kfJCn1pc/oeqH+KDKZxeUQDYmlhsvpeViKg1KcVi9bT887EevK74zVQ1SFe9g3wFlmWZrlobjVbb3Lk3nHGPWBuo1iEC/hWYuaobpIafGni/fRjqBpcfSKMCZj0QjmALYy9AA3zeMQIBDnosXYW9Z4nld3i7XRjqBPGZTQKAK3llrQlZiVBKrsLlXD1zOeul35t1lTskQBAEc4mVoKCyN6ns7PI4unvuuQc//PCDc0dHOJ0CJQarIC17FOyrQqm2fJk5htUFcTPWN7llB1Qlwvp/zYMx+4FwCEJ5Jr3lMZd3jYTBKECtktao9CQeqePP3TbwnXZhGNw4EJ022i4IDwB5OgEfnczDu+3DKyR2tjq7sDqmlegFhHqWs6XSIV31DpToqp7R8lqqq3f+7wTjw/qetn4n9XVw5irER4ULI+pAbxTgw5jnH9okCEMaB8LI0N2qRKBP+YPnP1F+uDiiDiKWydfQteTBX9Nw69k6TINViSM0SaaTI2sG1JsNVm4Pa2xsLK5fLysG7O/vj9jYWOzbt8+8/tixY4iIiHD+CAmnUmgvKNUCvVGQeDlDfMU/GdMN44pSGtZGsKWH1UdB2nnQnXIrtkRTo1Z5tLEKAJ3ryE9H3c1Iy+VNlgKARRcKIQhChaaTbLWQrG7CygvpqnegzBEgSKZyg60ETedEXbXWUUmolUhX+TXQNFVuax+VHd31FHxU8tc5mOERjgnk19ZdSVrmzKaScA/rvcP9ysbEDAnwYl3lvuqPPvooNm3aZP78zDPPYPny5Rg+fDiGDRuGNWvWiGr+EZ6JImE1Sm+qEKub17TeFQartbFsWdaqpp1AeUsczfL0NLrU8cddQWyVe6VliGRZ3SA1Wtbkc2EW6gWUGComdrayU1nZzt4srLyQrnoHShIddYykqxCrOErTC16JE2auJDGsNnS1hgJd9asChigvzzdgz1zdH+XLTMp6rRV/yNXmhGLmc5c3hp/VvKfenTKEzJAAL45h5Q4JmDx5Mvr374/S0lL4+flhypQp0Ov12LhxIzQaDSZOnIg333zTlWMlnIASg7XUIEjexoOthfXOemtRdATTNEuApsybuC9FXGvUMumqrG4o3/S4o1menoafRoW5LbQ4pK+N6EA1Wtf0w68JRbi3hi+evluaJKZSqfBrj0jE/XSbcTQp887m43y2NFZq8I4MPFY/AONbBDuclMF6oXHGw7iqQ7rqHRQoKRdokHo5rb14pYYyQ8UZjQNe2J+NF+6UwWtfy1cSEmBpj8VwxmcCQL6C2TpPZ3QDPe6uG4XEfAOGNgnEriQtig0CXmrB7nD1YotgXMvXY/FF+21sC/UCBmzPkCwfsTMT99XyxQf/CUe0jTrY6SXS6xx6Z/aMratksCIyMhKRkZHmz2q1GtOnT8f06dNdMjDCNShJDtAL0rdx66mr8pCAio/NRIkB+OOWVrLcMjkgJpC/0L23eFgBoG6AgOlxYebP99e2XREgKlCDN9uGYs6pfJvbATCXpLJmd7IWu5O1uCfcB48r6AFuGT1S3TwBvJCuegdKQwKsZxwCfFRQq8oTFQUABsH5M1cnMqQv+ZYhAUp0Na/Ue+5fjQoY17x8lqptLdu6qlKp8GrLEC6DdfvNEubyS7l6XMrVo8QA/NBVvgVzQr7UiWDS0+qWzMr1OlVcXIy6deti3rx5rh4P4WKUlF8pNQiSG8Law+rKkABrfCUeVj4c7VTiLdgrjM3LG0fka3uysPx7sZLyvFlYeSBd9R6UhASUGhjT8mqVpFJAqVHwaF2t7tir38rLxoRim+tNHQ4tsZWU5826ynXFAwMDERIS4vR+10Tlo8QToDVKQwKsY1hN/ZIrQ1gtPQHRCqauqkLQvythCevIpsrrzCYWsN3oRkHA2Syp58ZHBaRpVdiXrGVmuXqzsPJAuuo9KNFVAdKOg35qaUxoqaGydLX836zaqQQb62chIG777QySCg3YnST10OaVGnE4VYuTVt20AO/WVe5f54ABA7Bp0yZmADBRdShQEHdUamCXX7Fkzx1jxBmxVvawTA5Q4jXMd0Xf2CpEhJ9UWB+J8UfHaGUNBliVGQRBwPCdmXhkk7R8Vp5OQN+/AzFgRwYyGfVdvVlYeSFd9Q6UGKys7X3VKklL5RWXC13S8toaS0O5ur/cK4FVVUYF4PsuNZxy/J+vFqHtutv46arUA3ujwIDe2zKY67w5N4D7dWDo0KGYNGkS+vXrh9GjR6NRo0YICJDGs7Vs2dKpAySch84oLadiC61BGmtlHcMKABuuF1VK/2JLR2GkAk9AmJOmxKsqLA9rhL9asTfFlyHQ+1K0+DNJGm/MQ3V/kQBIV70FJY4AQBpC4KdRSV7qPjudj7fbhcHV+DsYMsXyMBJAqK/y540gCBID2JQopxRv1lVug7VXr17mfx8+fFh2u6ysrIqNiHAZSuJXAZPBatvDCgDfnCtQVPPTUSw9rPdH+aFekAZJRbYt5WAfFfrG8icKeSMsb3SEn1pSU9ceGsbmWxPZCQU8sCoSVDdIV70DpR5W67qcrFsxT1c5Max+Vgbr5NYh+PxMgd39Zj8Ybneb6oh1iTIeSo3i0IyKcCFbxzSAvQFug/Xzzz/3ygtQlTiXpcO3FwoQG6LBa61CFb8ZK6kQAJTFsFp3L2K9VYf7qWVbxzkT6wLX63tG4pNT+dhwnR20PvyuQLzYPATBDrzxehNsD6tKsbD6MO7/ioSCnMqUxl9VN0hX3U+R3oi5pwuQXmLAKy1DcLcDve6VhwRYeVhlpuJ/uGQ/C72iWMvDlDZhUKtU+OFSIbOkUt0gNV5uGcospUewnTr2KNYLoudbRUKEckoFJBYY0CjUufG0ngD3NxozZowrx0HYQWsE+vyebi4lojUImNFe2RuuUlFlVQlgmbyVYawC0n7bzSJ8sfTRmvhfBwOa/Xzbap0PvussXyqkOiHnYQ1VarAy7H6F70AizmTqYBQEh2u7egOkq+7n3b/zsOTfMsNw560SnB0WoziWU0mVAEDqYbX2cpq4xsgQdzbW5w70UeHtdmF4u10YhvyRgV1WIT8rukXiP1HK4t+rE46EBFh70osr6Fk/laHzSoPVIddTRkYGLl68iKKiImePh5BhT4ZGVPeOZ8rGGuvMVHuUMMqvNIuoWPN3R+Oe/DXStrAmAhhi72hcljfCapwQ4a9GkJL+toAkKQQoa9/rKPk6AdfyKCzABOmqezAZqwCQXGTEgdvKY7KVaqt1nKGvGngoxj1GYC0bseykrbZ5Jk7sZR7bPNihkABrA1Wpc8kab529UvTE2rlzJzp16oSmTZvioYcewt9//w0AyMzMxKOPPopt27a5ZJAEkKvnvwkKdEbm9L/S6VuWh7VxaMUCbXhq18WGaNC+ltgwHtcsRFYomaJK2a4iPu5Q7o1/675Q+KpVin8PrMtf0eoQpzL5upV5M6SrnkUGYxocKEtazSwxMKdrWfUwbWFtkPipVXj5Xml7ZWczsFEgLG/ju8I06N1APsafra0uGFgVZVKrENQOLLsgzSN88ORdQQ4ZrNYe1oobrN6pq9w/vd27d+PJJ5+Er68vXn/9ddFNa+rWsmbNGpcMkiiracnDqvhCNF6Tgrt/TMGaeHH8E6t4uy1YdVj9NCosfMTxsh1hjBJL1gT7qLCzXxT+7BeFtd0jcXhgbXx0v3y2LCtY3ZfqX4uY8P/snXd4FOXaxu/Znr6pm0YCgaWEHgi9K4ReRBQUBZQioMKnciiCInpEigU4gKLiAQ+ICAgiCiJVqgRpCkroJZCQkN6T3e+PZTdbZnZntmQ3yfO7Li7N1Hd3Z+555nmf0tQXfwxX4dQTYZjZSvddCk3CY/OwCr2mzDnD0nmnNkG66nmwze7fzCtHx+/TUf+b+3j610yLmQWhidnmrVxlIgb9YryEDlUwrzbzxZ9PheO73sH4sV8IjgxRIUjBLZYKlgcPV/hCbaRBgBS/D1Ph4KBQHBgUBh+pyK5GNUVm14PQfBNzzmaU1shSebwN1g8++ABt2rTBgQMHMHnyZIv17du3x7lz55w6OKISKWP74tNqtXj3dC7KNLpWqa8ezTa5aO3zsFrWC3TkDVvFo/WfTMxAxDBoGypD72gFmgRKrSamsK2rgfeqw8T5S6A2SigJ4PHyYAtHQgIAXSJhbYZ01fNgayO8/nIBrjwKX/nlTgl23TKtjiFUW81jXvUv2K7uyicTM4jyEaN3tAJdwuWs4ULGsHlY2ZbVZpRyEVqFyAzGvT1JlOYeVqHOBHOyS7W4w9KspbrD2/S4cOECnnzySYhEItYfJDw8HA8ePHDq4IhKuIq2G1NSAaQVVQphubayExUgfNpKF8Nqukwm0mXo20OgnEF/HiWmnDGdT/aqbZ5V+wjanq3Qv4O6ytpQoDZBuup5sBms5jkDS8/lmfwtVFvzSi09rAB7eSs+rOVZrF5o6SSKYbWP3lFyQduba6ujIQEAkMkR2lKd4X17yGQylJVxe0Pu3r1LLQY5KCrXYsWFPCw9l4ccO4v6sumheaA2W2bhfSMD1h4Pq/k+uo4sgg4DAOgZKceGXsG8an9S+GnVEOkjxlwBhcnvFFRgx40iVGi0+PLvfAzZnYGDqfY1DdBT4ODUV3WHdNUx9t4pxoLTOUh+YF+SCdtMDJ8wl1wzHRd6GVt2utL/V5j4dQ2XYU5rPwyu68UaY24OV/ksLtiiBWp5HxZefNRJKWj798/kIa9Mg9/TS/B/x7Lw1K+ZDo+hQGAiYHWA96WXmJiIH374gXVdQUEBNmzYgM6dOzttYDWJV49mYV5yLt77Ixej99l3IZZqLIXGXPTYHv73jQrrm9vK7cOsZ6Wy1WGViYWLaq9IOb5PCkGncHmVBexTSAA/RgmspTjmwEO0+z4Nrx/PwaF7jhmrgHM8CdUZ0lX7OZhajBF7M/HR+Xwk7XqAq3aU12N7pvMpKWTueDCf0rWlrXnl7HVYhToDdvYLxb9a+UMiYnh5PoVO57MdkzystqnjK0HrEP4VdU6ml6LrjnT0/SkDX/3jnCohNVFbed8eM2fOxNmzZ/H0009j//79AIBLly5hw4YN6NmzJx4+fIgZM2a4bKDVme+uVRa2/+1+KbLtmAZlc8yaX5CsHlZjg9VsvY+N+KUyjeVUhcwOD6uxfSutIrGrebeqa7Ano/VqrvNio2qiqAqBdNV+ph3NNvx/hRb44Gyu4GOw66ptfc41um41Wq2F4ettQ1stQgIe6aK94VYAP2NXqLHJ5pxwZIy1CaFxqDfyKuBgSoAJQmsDVwd4V5ZNTEzEpk2bMH36dEyYMAEAMHv2bABAnTp1sGnTJjRt2tQ1o6zGaFhcfXza7Zm3VmO79s0zCdlqAVoLCeDTkcN4Dwa60kZCBct4c6FTUvbC9r0Tlri7H3hRhRblGm2tfQiSrtrPzXzTF6fTPMMCjLWVTVfZXqKkItNpf2MpNQ8hkIpsZ9Kbn6EyJMDqblbRaat13ZMJjGGtnXelc3C3wVgTnQGCWiH07NkTZ86cwenTp3H16lVoNBrUq1cP7dq1g1hMdYTYYEtUsRZL+qCoAs/tf4jfH5TiqTgvrOoaCBHDsHoCzN/gzEtjAMA9Ew+r6TpbHlZzZGJdBqRQUTU2vJ3VL9kWNe9WdQ1CO/q4gvwyLZRy94/DXZCuOgdbHdMWn83F0nN5iPGV4OteQWgSKOUVagUA/lKRRYKg3vA113O5iIHQiYvKkAD77wM+3lOhCa21uAmdw7jbYHS0NJYnIvh9TiQSITExESNHjsQzzzyDjh072i2q9+/fx0svvYT69etDpVKhffv2OHLkiGG9VqvFwoUL0bhxY4SHh2PAgAG4dOmSXedyF2xGJJsRq2ftPwU4kV4KjRbYdLUIe27ryqfwEVY2D2uaSQyr6Xo+CVDG2CuqxmdxRJCFQA7W6kNNTA4QCumq41iTltv55Vh4Jg+lGuBKbjne+0MXPsA3JIDtRVvfXtWi9J9YeK3SypAAQbuZnpfHvkJfUMletZ8gKx3EqoKaqKuCPKwajQbfffcd9u7di9u3bwPQTVv17t0bTz75pCCBzc7ORlJSEjp06IDNmzcjODgYN2/eRGhoqGGbZcuWYeXKlVi5ciXUajUWL16MYcOG4dSpU9Umc5btoim2EgK48IxpuZS3k3PRL8YLpaxTV7ZDAox7VguNYTVHaqfBaqzdVRWwX/NuVdfRO0qOvY/6hQfJRZCIgPSiqns7113HtdeTSLrqHKzJ0m/3Skw0YdetYmi1Wg5dtVzIpa3+Ml05QWPkdsT521slwOS8LtBWMljtZ36bAIw9+NDwdxOlBJeyq64Vtbs9vK6At8GakZGBESNG4Ny5c/D29kZ0dDS0Wi3++usvbNu2DatWrcLWrVsREhLC63jLly9HeHg4PvvsM8OyunXrGv5fq9Vi9erVmD59OoYMGQIAWL16NdRqNbZs2YJx48bxHbpbYfWwcrz5sNXyS3mU+crm3TfvlmLLm2vuTbBVNNocGY84q1bBUou2cCZJV1X00unM4PWazvvtA1B4LBvZJRq83SYAM09m297JAQLlDLJKKn+gmiisfCFddR7W1OwgS0WLm/kV7LrKcj2yJbTqcxEsSv+JGcGx+nyqBKjkGqSVcG/gktkrsljtpn+MAi/F+2Df3RI8FiWHUibCpbN5tne0E0tdrcUhATNnzsSFCxewZMkSXLt2DSdOnMDJkydx7do1LF68GBcvXsTMmTN5n3jXrl1o06YNxo0bhwYNGqBLly5Ys2aNoRj+zZs3kZaWhl69ehn28fLyQqdOnXDy5EkBH9G9CAkJmHg4y2KZFroEohIeIQFsompisJqtF5wxKrbuYY30FuHg4DCL5caxZVWVdFV7TSDhqAOk2NUvFEeHqtCnjsLlYRvRPqbvyTVRWPlCuuo8uOIt99wuxuarRRbLz2WWseuqWcmpCo3WwosKVGq7eUiAzsPq3NmrbX2C0T3IenUOV9RHtRUXTHAjEzP4oL0Sp55Q4YP2SpuVIxzFUldr3lOQt4f1l19+wcSJE/Hiiy+aLJfJZBg/fjyuXLmCDRs28D7xjRs38OWXX2LKlCmYPn06Lly4YBDmiRMnIi0tDQBMprL0f9+7d4/zuCkpKbzHYM/2QrmcIwJg2t3p6q07CM83FcVyDbD9BntNzGN/XUWZxrKm2637D5Aiq/wubqRKAJjW/8stLDF8xvSHUgCVx8l5mAEGUmh5vkaLKsqQkpKCe8UMAMu+1w0UpUhJSYG/xAu55ZXHrMfkICVFV3/2XhH7vsYUFRcJ/l3U3gqkFFYqdktFIe9juPoacDauHq+mTAE7wtt5E6AthnEIwOVbqYj0kDaCfL9btVrtlPORrjqCqV6WlZSynnf5X3KwhZwcvXofbH0zsgtNj1NQbnkuAEi5cQuyTA1SCkw1TVteisK8IhhrrS0e3LuDlAINyorZx+qXcxuNfC2XG49TU8q+L9f2fPArsHx+ka7aR1am5fPZmQSiCMYm3f3sPMNz1904S1d5G6xSqRT16tXjXF+/fn1IpfxvUI1Gg9atW+Ptt98GALRs2RLXrl3DF198gYkTJxq2M29XaF7uyRwhD5KUlBSnPXi4uHmnGLhgetEEh0dCHWtqtOWVaYBj7A8MZWQsSq+nWiz3VQZDra5UXN+iPOC6aS1CjVgKtboOAMA7IxtILTCsi1KFwud2rkVoARfxId5Qq+vAt7ACSL5vsb5zbCDUan/Mf3gVr19UQAsg3EuEGV1i4f0om0CRXw6cTrN6Hi+xs4h9AAAgAElEQVSFF9TqGF5j0rPCvwQDf85AuRYIkDGY17UOQr1sx/5VxTXgTKpivF4X04FC7u5LjhIe6AtkVXq8/EPDoa4vrIGBK3DHtUC66gBH7pr8qVDIDVpnzFGz7fSIfANRqrV8oGtEEpPxpxdVACcs9S40MhrqcDnyM0qBM5Xtc3295IgJUwCp/Kd/ezWtiyCFGAHXM4Ac0/CFGF8xEuPV8PknBV+lig094tf1DIK6bpRhO/+rGUCu9WYeQn+X+lotPr2bjouPYi8/6aSEWh1lYy/SVTYiyvKB6zkuO34jVQAOP6x8vkPmDbU61mXn44szv1vebpTBgwfj+++/R0WFpSekvLwc27Ztw9ChQ3mfWKVSoVGjRibLGjZsiDt37hjWA0B6errJNhkZGRbeAU+GLVifLYa13Mqs6I28cvyaYfluUWI2FcV2Ln2c1Ufn8/Dl3wUm62QihlctVj2tHnXu4IqzahWse3vsGqTBLwNCsbyzEkeGhhmMVcB1SVcdVHLsH6Q759EhYbyMVYIdtjrBcxP8MamJj1OOb177tTaHBJCuOg+uWXiuUnrZJRpsvGv5MsBHVwHdfXL6QSl67nxgslwuYqAUMD8f4ytG0KMeqGz1iFsFV+ru4cE6jdvdPwRD6po6PVxRMlDEMNgzMBT/6aLEjqQQjG3kHA2ojbCFe7QLleH1Fr5OOX4dH9MLgK8jqjrB28M6atQovPbaa+jTpw9eeOEFxMXFgWEYXLlyBV999RVKSkowcuRI/Pnnnyb7NWvWjPV4HTp0wJUrV0yWXblyBXXq6N6QY2NjoVKpcODAASQkJAAAiouLcfz4cSxYsEDQh3QntuJK9bAlXOn58Dz7m7q5YcEWL1tUocWfD8uw4LRlFxiZmBFUKaD1I4NUwuGJ0QsrACSGyZDI0p6QTwyrvWFTLYJlaBHsuimX2gKbAflGS132+GeXCizWCcW8nFpNjLXiC+mq8+CSlvhAKc5kWM4YXMoux7lsSyvPvF41m67ql7/7h2WColQMKGUCHAFGusnmP2gVUqlpQQoxnm/IbjS6qvmGn1SE0WoyVB1FzPIOM7u1H3pGKRCiEGP27455X6PMDdYaqKu8Dda+ffsa/v/MmTMm6/QB/cbb6Hn48KHFMgCYMmUK+vTpg6VLl+KJJ57A+fPnsWbNGsybNw+Abspq8uTJ+PDDD6FWq9GgQQMsXboUPj4+ePLJJ/kO2+3wTbqy1kyATWwBS4O1kMVNW1IBfHCGvWWhTKQ3HmzHD/pIGLQN5fawBsgYqLxtv+LzqRIwjy2wjKgyXP1mbt4ONq8GCitfSFftwzzRCeCuDOLPITrnMtl11VyfuTysBeVaVm2Wixgo5fw9rD0iK2NE2bxwDQP4PabLWJ4rxr2vnmng/rCb2gzbb6u/TvwFvOBwEW1hsNa8mSveBuuHH35oNcZJKAkJCdiwYQMWLFiAJUuWIDo6GnPmzMH48eMN20ybNg1FRUWYMWMGsrOz0aZNG2zbtq1a1Qpkq8O65VoRxjT0MSkubS0kgAtLg5XFw1quNel2ZYxUQEjAkg4BhmkrtqLY/jynwNhCAl5s7IPrueVIzijF03He6KAiL6k7EdoDWyjmBmtBDRRWvpCu2geX1u29UwwJA/SIlFe2XxVY465Cq6sMoC+yz2Ww3srn0FUx/5CAJ+p54bmGlYYkm23NV1vZnjULEv3x4bk81PWT4PUW1ef3rYmw/bb664Rt5rFruAy/3efXbhgAon1NzTlX67g74G2wvvDCC04/eVJSEpKSkjjXMwyD2bNnG3prV0fYPKwn00sxdE8GdvULMYiqNQ8rF+Z1VdnOpQWQxlEEXi5mePWS/2O4CnH+lZcK2y58e9KzhbDW9RXjw45KXvsTrsfcUWPNKx4st2xZaQsKCaiEdNU+2LTuck45RuzVJVFNbeqLf7cLACDcYAV0cazej4wIrpCAG3nsReBlIvDysHZUybC2R5DJMrZpfb7ayhZ+9kozP7zSjAxVT4AtlE5/nbB1OJsU74vf7rPPpLAR5mWmq+Vam8mU1Q339g6rBRRxuE6PpZXieFrl2xNbi0BbmE9dcQnrXY6SQZUhAdaJMJvqZ7sB+CZTse3rLbTxNlGlGHtE9WEherpFyNGeJVaZi4QQqYWHtTYbrIR9cGmdnpV/5RtCKuzRVuO6q2yGIMBtsMp5elgfi1JYLGOTY/P7hYtCuo88Gja70f/Rb2teQxUAggW2dpWKGCiMHtUaLffsQHVFUGvW7OxsbN26FdevX0d2drZBEPQwDIP//Oc/Th1gdcfaBbP/bgk6hcsBAOX2eFjNhJSrdzDXkaVi9pCAwbEK/HCzGACQFC3n1RFL4UD2v1cVtWsl7MPH6PV/YTsl+ux6AC103vJZrf2QVqTB4N0ZAHTe9+6Rcuy7a1lehwGwtIMS6Wa9iWtirJUQSFeFw+dBXFiuhY+Usc/DaqStXCErN/K4Q62UcktNm5vgj/f+0OUTyETAyPqW9ajZ4hz5OBUAoNBK4i7hftiuWX3YSdtQqUnr1tda+KJ9mAz1/MS4/ug661tHgd23i1mPPaGxLinORyJCcUXl9ZpfpoUP/6p4Hg9vg3Xv3r0YN24cCgp0WcJyudxim9ogrNklGsw9lYPL2eV4Kd4HgXIRPjqfhygfMd5pG2CReGRNWJeez8PBe8VQB0gxrK71YvpsWFQJEChYchF7lYDVXQPRQVUIjVaLFxrzyw51pFyVT1X1ayXswtjDkxgmw65+Ifjtfgl6RynQSClFIyWwIykEx9NKMCDWCz/dKrIwWIPkImx8LAgJoTIcuW+6bu/dElzOLsPGK4U4cr8EA2K8ML25r8unsio0WnxyIR+7bxehR6QCTzinuowgSFcr2ZBSgP/+U4DmQTJMb+GLRWfzcCOvHK8280NSHVNvJB+DtcuOdKzqGsjaftUWxtrKpat3OGau5CL2l/CekXK0Cg7GqQelGBjrZRFzCLBPDZOHtWZgLVafYRjs6heCdZcLEawQYbTaGyKGwS8DQvF1SiHCvUToGiFnNVg/6qjE84/ioH2lDDKN5PWrfwrQLkyGJefyEOYlwsJ2SkT6VN+Sj7wN1lmzZiEoKAibN29G27ZtIZPVzsSYFX/m4X8phQCA3w+ZBkRrAKzpZhqTZGvqKvlBGZIflOFKjvBC7ebtAm2dyxypSFeyxBwfqQhTmgp7eiscuAfIw+rZmD8wO4XLDTMDerpHytE9UrfsTIZlosCbCX7ooNKtZ4vJa/d9ZV3Q5AdlSAiRonuk5ZSpMzl0rwTvPvJ4nXpQhpDGYjRx6RktIV3VcS23HFOP6EpEnXpQhrX/VJZPu5D5EGeeVCHYSGT4GKzX8yow9sBDu1oNGxusQqdVpWKG9WUrv0yDx6MVeDya+7oWs+zHt/RgTZv+rWlwzYDqCVKI8X9miXGhXmK89mgZ20xB8yCpiVPJXKs/OGtaElMmysHn3U1tlOoEb9dWamoqXn75ZXTq1KnWiioAfHg+n3Pd5qtF0JhN5/EVkVMP7DBYeRa45kIuZjDCbFpqeD3hnl6AvXIAFx3MYh7bCYiBJFzPG2aiKTS7WM1Shse4RiDbS5I575/h3yXIXl49alpD8+3LVX8dkq7qWHqO+/fOLdPip1umnqWiCn5u07QiDacn1BrGeYRCHQH6jO/4wMr7QMyY1lPlgs0oEfM0uJd0DDD5+71EKg/oSZg3eng8ynI2xRpSEYP6/qaeoVizdr22tPW7a0VW1zuDtMIKNNp0D80230frLfcx6bywz2kN3gZrixYtkJWV5bQT11Su55qKI1vXIGfxoKgCv6eXoOKRyAk1WGUiBo2UUkx95E2N8hZjWnP75kWFxLDOSfBHoJyBmAHeTfQXVLOQcD1jG3kjXql72A6OVaCPFY8QG+3DZHjC6MUnKVqOnkbeUj6l1K7ksCe0OJMMs1jaYk3Ve/pJV3U8tFFpIsXsejCfXXI2R++XIPvRmIQ7AnT/XdReiWC5CAx0taUDeCRiWWsgY4vBsV7oHK4zijuqZHiOo8EA4R7q+kkwpanuN4n2EeOtNsJfKOa3DYBcpLtGQhUivN7S1JnAJ3zEPEbe2RRXaA0vitfzKnCvxHm6yjskYMGCBRgzZgx69eqFxMREpw2gpnE2sxT1jTxMQuNKhZBaqEGfXRloEyLFLwNChYcEPBLWf7cLwJsJfpCLGN5v8+YIiWHtFiHH5ZERKNeAV0IXUbVE+0pwZGgYisq18JawT29aQ8QwWNsjCEs6VODK1Wto39S09zjfmDxX4wk5KqSrOmwlnf750HQGSqjWCWXWyRy8dSoHewaECg8JeKShXSPkuPR0OLTgr49sTWX44i8TYWffEBRXaKEQMxDVoHJGNYX32ynxVkIAxCL2BDtbDIr1wv4ORfCJrIc6PmKL5zUfZ8DDEo1JeI2zMS+M5Ey5522wtm/fHgsXLkS/fv1Qv359REVFQSy2LHe0efNm542uGnIpy9wT4Pqn4umMMvx8u9guD6seb7ZofwEIrRIgFTG8ul4R7kHE8G8qwUWwQoyHLLOgQtoBuxJPMFhJV3XYSoy6mGVqsFaFrpZqgEVn8xAqsLyQsa4KCZUCHPccixgG3h5yfxHsKBz8fWQinbeWDT6RMvcKXWuwmteUZ2tJay+8DdYdO3Zg4sSJqKioQFpaGoqKLGMhalKBWnvJNiv6V+TiqSs9n18q4CxfxYUzvZuOVAkgahd8dKIqbEk7qh05HdJVHeU2piktdbVqfrzdt4sFx/U7oquOeFgJwlZiFwCkFVWgGVxX68rcYHWLh3X+/PlQq9VYv349GjRo4LwReCiXs8uw+Fwe/KQM5ib4834jMa8pWRWeAEAXzyoUZ76Jy6tvpQzCA8ku1Zi0x6yp1DZdLS7XYum5PFzOKcP4Jr7oFqGvQ21jvwpd2IC+E1RV6SoAXM0VFk/tiK7a0/GQIPTwqWnN1ardWZjfyxLGedc0b4M1LS0NCxYsqBWiqtVq8dSvmYbC0A9LNFjXM5jXvuZde1wda6XnYrYwURUz9sXQcMHWC5kg7EWjBR4UaxDuXXVvQuIq8euaUpt0FQD+81c+lp7XVQT4+XYx/nk6HEEKMa/i/vllWkNBfnt1VcQI96yfzRRWwYU8rIS7yONRi/d+oWsbtVh4WJ0YEsD7UK1bt8adO3ecd2YP5u/scpMuJjtu6Eqq8OlGlW8mpFXpCRCCgyGrSDRr0dm3jmtrZhI1i44q2yV+LldBpQBj3DHzXpt0FYCh0xOgi1td+4+uprUtDytg6j2yV1cd6cjHF0c8rOMamWb221tmkKidmNdxZeOyHTXfhVBq5sB1ZkgAb7Nl8eLF2LJlC3744Qfnnd1DYXvLvZlXbmibZg1zlzzbsWQi3Zu+UFRezntVYStQLYR32gbAX6Y7xog4L141BglCz79a2hbWU+mWDQhciTvCsGuTrrKR/KAUN/PKLUqMsWHsDGCLYeXTgEQicv3v7IjBOijWy1CnOtpHjDmtqZYqwZ+BMQokhFiPT/31jmXbbGdiPlvizBxA3iEBkydPBgCMHTsWAQEBiIyMZM1mPXTokPNG50G03JLGazvzkABzg/XIkDCoAyRgADT45h5yBbTTSx6uQvy393m5/ZsHSVFYrsHVXPYHgaMXUadwOc4/GY7sUo1F8WKCsEXPKAVWdVFiypFszm3e/SMXT8Z5IZYjI9bZuKNoRW3X1d23izn7o5tjrK3mHtY3WvhhegtfeIkZzP49B2suFZjvDkAXuuSlYJBWZOpYeCneB59eZN/HnDENvbHuciHnekc698nFDH7sF4Lb+RUI9RLBl0qpEALwkYqwZ0AoQtelcm7zsESD//yZh5ebCWsIwxdXGqy87waZTIaIiAi0adMGDRo0gLe3N+Ryucm/mtKpxZFZfGNRrdBoLcq1NA2UQC5mIBMziA8UlqnnJxXxMlYBIFghQqtg7t9D4oSYU6VchLp+klqRxUw4nygePa03XOE2DJyNO8Kwa5OuOopxL3bzGNa6/mL4SkUQixg0D+LWVZkIaBNq+n0qZQzkAn588/3NcTSZVSJiUM9fQsYqYRdSEYNoG9q6xEpnOUcpNa/DKnJD0tWvv/7qtJN6Oo4EvhuHBJgfRyE2LVHjygLqXmLrdTSpChXhbvjU/r1nR1tNe3GHeVCbdNVR8kw8rKbrjL2a1tpTSkUM5iX4m7R6XZAYgNv5/K+zMBuhWdQMhXA3tmpd55VpodVqXeJs8ggPa23CkUSp/EcXQplGa9Ef2zzg35Vv0N4SBhnF3JkMjiZdEYSj8Hmwf51SiNePZ+NQKr9pY0egQheejX726sj9Enx/w7RerbG2WnMESEUMmgRK8VWPQPSto8CU2FI808Db0IaVD2E2ShxS4X7C3djSVo0WmH4sG8su5PGq0CEE81lltyRdAUBBQQFWrFiBp556Cj179sQff/wBAMjKysKnn36KGzduOG9kbsSRUlTlWt3b/6Izefj4Qr7JOvOLyKUeVgmDm3ncSWKOJl0RhKPwfbB/+XcBhu7JxOVs12a3umvWobboqqPkl2lwLbccg37OsFhn3D3I2syS7NETb1g9b2x6PBjj6pRDImKQwrPWKgMgyEbnKzJYCXfDxxmw7nIh3k7OxcIzuTa3FYK5AeyWslZpaWno3r075s+fj8uXL+PcuXPIy9N5EJVKJT799FOsWbPGeSNzI46Wosov1xhqDRpj3g3Kz4qwBslNf5rh4bqHdddwfvFsPhIG4xv7cq4nDyvhboRMnWoBbL5q2QXKmbjDzKhNuuooBeVaLDqby1ot19jD6m8tJIDjrWRkfW9eY/CRMAhRiKxOc1JIAOFuhLw0fXQ+3/ZGAiitMA8JcJ4Hl7fZ8vbbbyMjIwP79u3Dvn37oDVqpccwDAYOHIgDBw44bWDuxLwNoFDMKwXoMc8eVXlxTy1NivcxBE6rvEQYG63zAIxv4svLExTuLcbAWAXqcGTwS8jDSrgZoZ6ovXeLkePgvWkNsROFlS+1SVfNH2RCySvT4s+H7F52Y4NV5W0thpV9eVIdBer56bTS2mWp8tZl7pvXS+UaC0G4A6HamlpQYaI9jmAeEuCWGNa9e/di0qRJaNWqFWugbr169XD37l3njcxNzE/OwevHcxw6BpfBau5htdbFJ8pHjCNDwvBTvxCcGKZCuEJ3zCF1vXDqCRV29w/BrFbcZSlUXiKEeonx2+Aw7OwbYrGeNJVwN0JF9VxmGepuuIfXj2c7TVyNcUcMa23R1eNpJVBvuufQMfLLNJy9yIyNxCC5iPO35OruFygX4dDgMPzYLwSXR4ZzjkHvZFjcIQAHBoViQmNLw1VEzgDCzQj18sdvvo/236fjdr7jzVo8IiSgoKAAkZGRnOuLi4tRUVF1Gb2u4GpOOT654Lh7nKufr/lFFGHFEyATMVDKRegULkegWXhAnL8EHVRy+Mu49494ZAwr5SJ0fdSv2xgxhQQQbkYqYgS/fWuhi2m9IrC/Ox/ccUvUBl0FgLm/5yCn1MFQKysl/YwNVhHDcP6W1lpI+8tE6BIuR5CVpCq9rjIMg9YhMjQIqJoawQQhBG87PFKXc8rxBUf9YiGYT4K5xcMaFxeHM2fOcK4/cOAAmjRp4pRBuYtNV51T85GrVqq5h1VlxcPK563kyTjutn3Wjq07PnkBCPdjrwkjpAwRX9xxS9QGXQWA0xmOJ8xZNVh5PhX5env6x7C3mjYPN3AgP5cgXIa9cdS3nVBGsNTcw+oOg3X06NHYuHEjtm/fbpiOYxgGpaWlePfdd7F//36MHTvWeSOrQgrKNFj5Vz4+YkmUsodFZ9mz7sxjm8KtxLDyMSjDvMR4pRl7YlWEDYOVQgIIT8DesEYhtZJ/u1eCxWdzOeMf9bjDYK3JugoAP98qwvtOykLed7cYF7PYPetWpNQEvi/qC9sFsC6PMDtRhZNLAhGEM/BhyarmE4IlRFcziivw8fk8fHOlEBqjEK1yi5AANzQOmDx5Mv7880+MGzcOgYGBAICXXnoJmZmZKC0txdixYzF69GinDawqeXb/QxxMdV5/3eQHthMDACBAZq38Cj9hfVbtjRV/moYxyMW67i3WoKQrwhPxEjOsfeLNKebp2jpyvwSDd2dAC2Dx2TwkD1ehLkerV3e1Zq2puvrd1UJMOJzltONZ6/JnPnvFtaWMp2Eb6ydB21CphZab5x2Qh5XwRNg8rPX8xPiL44VPD19d1Wi1SNr1wND6/XZ+Of7Vyh8AUGrmpHVLSADDMFi1ahW2b9+OoUOHomvXroiLi8NTTz2F77//Hh9//LGgEy9cuBBKpdLkX8OGDQ3rtVotFi5ciMaNGyM8PBwDBgzApUuXBJ2DDxnFFU41Vq1hfhFZ6zLhb8Pg1MP21qTyElscu3WIabvCPnXYp7wIwp3Y6iKkh68n4N9/VJZBKtcC7/3B7e1zh4e1puoqAKcaq7Ywdwa8yJIMBVjvgmWOeVUXwDLUqmWwsPbaBFEVsIXIxPnb9k/y1dWT6aUGYxUA3j9TOTttHhLgTIPV6if45ptv0KlTJ8TGxhqWde/eHd27d3fKydVqNX788UfD32JxpRgsW7YMK1euxMqVK6FWq7F48WIMGzYMp06dgp8fd3a8UKzFRTmbMJaC0++3C8Cc302rEjQMkCAhhF+9VTaDlS3U4IN2ARi8JwMlFUC4lwgTmnCXZSEId6GUi3CTR3xqMc9Qq+NppSZ//3Kbu2MWT+ebw9QGXa1KguQiiM3eNl5u5otNVwqRa6TvYgYYYSXu3xw2bTVPlO0ZKUfzICkuPCwDA+CL7oHCBk8QLoAtVMVaCKIevgbrgyLu8oLl5p2unDh1ZdVgnTp1Kj777DMTYXUmEokEKpXKYrlWq8Xq1asxffp0DBkyBACwevVqqNVqbNmyBePGjXPJeFwNWxmrKU190TJYJ3hSka5da1K0AjKeQaZsXoBwluoD7VVy/DY4DH9llaF7hBwBVioMEIS78OcZof/a8WwMiFHYTC40R2/AsJXFqqooGdJV58LWgCXGV4Ljw1Q4fK8ED0s0kIuAjio5mgbx94h6scQBmtfOFjEM9gwIwd47JYjxFaM1T0cDQbgStnrV5mEzbPyRUYadN4swKNb6ix3by1xRuRZeEobFw1pFjQNcUevQmBs3bqBJkyZo0aIFXnjhBUMLwps3byItLQ29evUybOvl5YVOnTrh5MmTTh2Do8WsASBQzu9Jx5UI1TlcjpfiffFiY188Xd8bSrmAaSuOkAA2GiqlGFbP22rZFoJwJ0IM0GV/2k6SZHuhA9hjD6tqrqU26GpVwtXiOspHjFENvDG1qS/GN/EVZKwC7B0P2Yxjb4kIQ+p6kbFKeAxs4YblPHVn+tFsm3YRmyc2rUg37eXKKgFuKyLXtm1brFq1Cmq1GhkZGViyZAn69OmDEydOIC0tDQAQGhpqsk9oaCju3bNefDolJUXQOFKu3wTAf5qIjVBJBbJKbBuZZZmpSDH3lwuA+7OZthWsKMhGSsoDu8/jDIT+Du6kOo0VqFnjHRstxX/vVBoSwwMeYgvP+3HVXwUYF2j9OvcWeaGowlQxU1JSHoUUmN43Gi3/71atVvParqrxGF1NSYH592sPDLTQ2miaKy4vceie4No3LUcO80CRK1eu2H0eZ1Cd7v3qNFageo3X1lg7SRgY2zXPRJbhwcNsALZf2jJLNPjx7DU09+e2Va6niwGY1ndPvnwTZf4aZOXIYGxaSkXO01WbBqu1xCBH6N27t8nfbdu2RatWrbBx40YkJiaynlur1docj5AHSUpKCsKjY4Czjhl39YO8cbmAOzZOT2KjWM4MZVukpKRwf7Yjpp1w6qmCoVa7Lx7N6lg9jOo0VqDmjffN6AqkHsnCnw/L8GJjX/RrGQWc4d/ZqU69BlZrcAZfSENmjmlmrFqtRnaJBjhuaqRptFVniNZ0XVWr1Ra6ZA++UpHV6gAAEOrvDbU6xq7jW7s+Sy+mAzCtEuDOe6863fvVaaxA9Rovn7GqASxg8rDmUgEaKSV4q0s4FpzOBcCv1nyebzjUau48l+PaAuBytskySVAk1PW8IL+VCaDSHpIwzrtvbFpPU6dOxSuvvMLrYAzDIDU11a6B+Pr6onHjxrh27RoGDhwIAEhPT0d0dLRhm4yMDAvvgKMIqTvGRbQvv2lMrql6Z2OtAxZBeBKhXmJs7m3ZOpgvf2WVoU0o91QsWyx4XpnGon0gAFRlP6marqvOwFvCwFfK2DRYuUICHCWXJQ6QIKoLrzb3w6vNKx1X5lP11jibWYox4DZYC1liqu4/Cgkw19YqqxIAAG3atEHdunWdd0YOiouLkZKSgq5duyI2NhYqlQoHDhxAQkKCYf3x48exYMECp57XGTGsdXz4GaL2dp8QSiMltQskqi8ykWV7Py4uZVs3WItYQnAeFGlYExCqsgZ8TddVZ+AjYeArFQGwfjF0Ulm2nnYGvaMV+PLvylaV7axcZwTh6SSEyLDlWhGvbf/Otl6vtYjFYE03xLCaLq/SxgHjxo3DiBEjnHZCPXPnzkXfvn0RHR1tiLUqLCzEqFGjwDAMJk+ejA8//BBqtRoNGjTA0qVL4ePjgyeffNKp4yhxwlMqmofBOsmFZaTebO2Hfz+qg9YiSIr2YSSsRPVlRZdATOJZwzOrxLoxwyaseWUaiBjLWYiqNFhruq46A72H1RqhChGea+h4rCwbU5v6YkNKAYordDV6F3Vg735FENWB5xp6Y8m5XGSV2BY6W7pawKqrumVu9bC6itTUVIwfPx6ZmZkICQlB27ZtsXfvXsTE6GKRpk2bhqKiIsyYMQPZ2dlo06YNtm3b5vRagSVOmAeMsmKwjm3ojU7hcgyv51hilzXeaOmHhkop0osq8HR9b5fFxxFEVfB0fW+EKkS4nFOOWSdzrG6bY0N82aau8su0SC2w7EZXlSEBrsJTdNUZ+KoYI0oAACAASURBVEgY+HI87XpEyvF4lBxPxnnDm6X8lDOI85fgwKAwHEwtQUeVDK2oCgBRjfGTinBkiAo/3CjC7N+t62q2HY6A/DIttFotjtw3rX1dI6oErF271up6hmEwe/ZszJ4926XjYCtdIpRoX/avcUCMAp90dn0haYZhMKSu6wxigqhqekUp0CsKNg3WbBuxA1wG6+vHsy2W14S28J6iq87AR8rAh6Pq+H97BAkq/2cvTQKlaBJI3ayImkGUjxiTm/pi3qkcq22Fbekqu8GqwbbrliEHzmwcUOuzcxw1WKO8xVB5iVh/FJ8qilkliJpK53DrXi1rwlqu0bLGwj4orsB9lk4tFVq6X52FM2rNzk3w52zVy1a4nCAIfqzoYt2RVlwBFFuxaAtZcgPyy7TYw9JJUCV3XvKiVQ9rVlbV9YJ2F3wy5yQM0CJYigfFGjwoqkC7MDnOZ5aCYYAlHQMgETGID5TiXKbpNGNVJVkRRE3lrQR/jDv4EKmF7KJnbeqqiONl9ERaKas3tao8rLVDV/ltV99fDB+JCBezytBQKdHVws0px6gG3ugaIcflHMvkDzHjXK8NQdQ2BscqsP2GAr/eKQaXzy67VINwCXu4I5u25pdpcLfANLCqe4QcITJ+pbT4UOvTyfn0JH8wJtJmXGirYDJYCcLZtFfJcfHpCJzNKEWPnZb1kq15WNmmrQDgyP0S1uVUxMh58CkX+F3vYPSOVljdplWwpYfdW8JQnD5BOICPVIRvHw8GACi/Yq+XnF2qYW0nDwCFLKXm7hdpcCff1KD632NBuH/DeS/otf49lU9ZKz7iyCWsBEE4Dlcf7GwrSVds8asAcCOP/S3VCeHsxCP46Cpbm1NzmrG0U3VG3gFBENaxNntVyHIP3s6vMGlv3cBfAj8nT4XUeoPVWeLXKsRSWLn6mBMEIQyu2QprHlYug5WLmpB05Snw0VVfHg8zLwljoaNUz58gXI89s1fGhHs737ys9QarkO4P1ohnySTliqEjCEIY3B5WDWeCDx9RNYbsIOfBx6jk26GqITVCIYgqx9rsFR9tdcUMc603WJ3RmhVgf6A+YMlEJghCOFzOuHItexFrQLiHld4vnQcfXeUTEgAAjQLIYCWIqsbR2StX5PDUeoO11InVwkerTTuuUG1UgnAOATJuqeLqNS/Yw0oGq9PgE8PKJyQAAF6K9zX5eyjpKkE4jefU7J3i8qyFBPC4v13R0KPWG6zODOCf2coP9fx0WXWPRcnRLcI1Pa4JorYhFTFY2iEAbJEBXPew0LbLGjBOqR9a20nOFiGj2PbskpV3EBMSQmUY86j9aohChFeb+drYgyAIvkxr7otYX8tqANa6gPKxm1wRElDr51qcabDW8ZXg5DAVcko1CFaIIKLSKwThNMY38cWI+t7o+H2aSV1WrulnPl4+czRasBrFBH/+dUmOvD8zbW4npDTVss6BmJvgDz+pCAqqvkIQTqNBgBS/P6HCv//IxfI/8w3LrYX18JmZdkXSea31sN4rrMDhTDE2X7NsJWZMc5ayKtaQiRmEeonJWCUIFxAgEyFEYeoN4OrIYk98OsWxei6hXmIyVgnCBcjFDKJ9zHSVQwy1Wi0vbXVFDGut9LAuu5CHt5NzAdiesl/SIcD1AyIIgjcKszd3rngqe+LTyWC1n4fFFfjtfimvbbni5giCcA/mL4NculquBfjIpCta09dKg7WRlTIpvaPkGFbPC4fvlaBPtAIdVBSHShCehIWwlmux7EIefrtXgjh/CaY09UVdP4ngGFYAqNBqAZAXzx4C5SK8cTwbeRXs39+P/UKw+WohguQivN7Sr4pHRxCENcwdASUVWhy5X4IvLxWgQqvFyAbe6B/jxTuMkjysToKtK5WeV5v7oWuEHM+ofapwRARB8MUsIgAr/szHgdRH7VbvluBMRil+GRBqV3w6eVjth2EYtAyW4te7lq1vn67vhS7hcnQJJwcAQXgi5qU57xZUYPgvGYbkqx9vFePw4DBE8GwIQGWtnES4txihCvaPbv6WQRCEZ2F+jx5MNTWQTj0ow428CrsMVipt5RhcMf+kqwTh2ZgnSZ1IKzWpFKDRAt9fL7RaPcAYahzgRLgMVr7dVwiCcA/mxg+bjXk2s9TOGFayWB0hmFNXa+2jhiCqBeYeVnZdLePdHdQVVQJqZUgAAATIyWAliOoIn0zxsxlldk3vV1BzOodQkq4SRLXEi4c1eDajjHf1FR8X3PO19rVXyVG1mjwBBOHZsLVBNufCwzI7k67sGRGhh1NXqRwVQXg0fHQ1s0SDm3n8pq6oDqsTIU8AQVRP+AhherHGrsYBFBLgGNy6WmsfNQRRLeBrYN4pKOd3PIphdR5KmeWXKRfrWkASBOG58PEEZJdoqHGAG+CeuSJdJQhPho+uAkBaEb+4KUq6ciJsngBfSa39Ogii2sDnzT2nVGNX0hVVCXAMNkcAQAYrQXg6fD2i6YX8hNXbBfZUrbXQ2DwBJKoE4fnw8QTklWlRWC48g4pCAhwjkEICCKJawtfDep+nh9Wf4+XVEWqtirB5WF2R1UYQhHPhG2uVXmyPwSp4F8IIbwkDCWP5JZIzgCA8G966WmTbw9oiSOqSl9Taa7CyeFj9yAtAEB6PXGx7GwBIL7RusHaLkKOJWZtmKmvlGAzDwJ+lPA4ZrATh2fA1f9J5eFj3Dgx1cDTseIyF9uGHH0KpVGLGjBmGZVqtFgsXLkTjxo0RHh6OAQMG4NKlS045X6DcUkBJVAnC8+HrCbhvwxMwvJ4XzHMsa1pIQFXrKgD4Sdg8rB7zqCEIggWG4amrNmJYE0OlvMMLhOIRKnLq1CmsW7cOTZs2NVm+bNkyrFy5EosWLcL+/fsRGhqKYcOGIS8vz+FzRnpbumlcUTeMIAjnwlcMbU3vtwyWQmwm0jUp6codugoAKjmFBBBETaXchka2Cpa57NxuN1hzcnIwYcIErFixAkql0rBcq9Vi9erVmD59OoYMGYL4+HisXr0a+fn52LJli8PnjfKxNFhteWQIgnA/YiepVnyg1OJYNSWG1V26CgANfSynDH2ocQBB1Apahkhddmy3G6x64ezevbvJ8ps3byItLQ29evUyLPPy8kKnTp1w8uRJh8/L5v5OyeFXEJcgCPeRU+ocq1ImZmDurK0pIQHu0lUAiPWyNFhFPKcbCYKo3rjSw8qje6zrWLduHa5du4bPPvvMYl1aWhoAIDTUNHg3NDQU9+7d4zxmSkoK7/P3CJbhYGblVzA4tFTQ/lWNJ4/NHBqr66jt45XkigAoHDpGE98KpKSkoJ9SjERvBiIAYgYoS7+NlBzbRqtarXbo/K7E3brawt/UOJUxWo++Zj15bObQWF1HdRqv68bq7fARxBk3kJJpuozveG3pqtsM1pSUFCxYsAA///wzZDJui9zcE6rVaq0GBwt5kEwrvoIT2RIUV+jiVycnRkEd6Dp3tiOkpKR49EPSGBqr66DxAg20Wnxy5wEuPCyza38xA3zaKxzqYBleNxpadftu2fAEXUVKCh6PkuPXuyUAgFeb+0Otjua/fxVSnX5zGqvrqE7jdeVYF5XlY+bJHLv3nxzvgyYNo0yWOXO8bjNYf//9d2RmZqJjx46GZRUVFTh27BjWrl2LEydOAADS09MRHV0pdhkZGRbeAXuJVmhxYpgKh1JL0FElQ0OlZxqrBEFUwjAMfu4fgp03izH5tyze+x0eHIqzmWVoHyZDoxp6r3uCrgLAhseCseNGEfxlDJKiHfOGEwRRNUyK90UjpQTP7nuIAlvZVY94p60/6viIIRcz6Bfj2nvdbQbrgAED0Lp1a5NlU6dORf369fHaa6+hQYMGUKlUOHDgABISEgAAxcXFOH78OBYsWOC0cdT1k6BuI7dGRhAEIRBfqQijGnjj7eQcXnUBAaBhgBQtXBhf5Ql4iq7KxQyequ/49CJBEFVLj0gF5ib4Y/bv/DytPhIGT8RVzb3uNktNqVSaZK8CgLe3NwIDAxEfHw8AmDx5Mj788EOo1Wo0aNAAS5cuhY+PD5588kl3DJkgCA+Db/I5A/4NB6ozpKsEQTiKt4CqHooqrADi0a7FadOmoaioCDNmzEB2djbatGmDbdu2wc/Pz91DIwjCAxCbV/7nwFfK8C6MXdMhXSUIwhpeAozQquwQ6lEG665du0z+ZhgGs2fPxuzZs900IoIgPBm2HgIMAPPoq9pcuJ50lSAIIbAZrI2VEvydbVn6syq11e11WAmCIOxFwuI1ZRNQag1KEATBD7aQgCA5u4b6VmFIAKk4QRDVFgmLgrEZrNRpiSAIgh9CDFafKnQGkMFKEES1hS0kgM2bWptDAgiCIITgxSKsQQoODyuFBBAEQdhGwpJ0RSEBBEEQ9iPEw+pHBitBEIRtWD2sLGJblaJKEARRnWFLuuKMYaWQAIIgCNuwJ11RSABBEIS9sHpYWUICpCJdk5CqggxWgiCqLWLeSVckdQRBEHzwZtFLNg9rVSezkooTBFFtaaKUmvzNgCuGlTysBEEQfFCwdAUMZDFYqzo3gAxWgiCqLW+09DOJY/2sWyCFBBAEQTgAwzDoEy03/N0tQs5qsFZ1boBHdboiCIIQQqSPGLv7h2LztUK0DpZiRJwXrudZdmOpyvaBBEEQ1Z3PuwdhxYV8VGi1mNbcD/llGottqtoRQAYrQRDVmsQwGRLDZIa/ycNKEAThGAEyEea28be6DYUEEARBOECUt2UAVqQPS1AWQRAEwQs/KWNRMrCqdZUMVoIgahSPR8uREFKZjNUrUo7EUJmVPQiCIAhriEUM/tXKz/B3gIzBxCY+VToGCgkgCKJG4SsV4deBobiUVQ6pCFAHSMCw1GslCIIg+PNqcz88Vd8bqQUVaBwoYS1/5UrIYCUIosYhYhg0DZLa3pAgCILgTbi3GOEsYVdVAYUEEARBEARBEB4NGawEQRAEQRCER0MGK0EQBEEQBOHRkMFKEARBEARBeDRksBIEQRAEQRAeDZOdna119yAIgiAIgiAIggvysBIEQRAEQRAeDRmsBEEQBEEQhEdDBitBEARBEATh0ZDBShAEQRAEQXg0ZLASBEEQBEEQHg0ZrARBEARBEIRHQwYrQRAEQRAE4dGQwUoQBEEQBEF4NGSwEgRBEARBEB4NGawEQRAEQRCER0MGK0EQBEEQBOHRkMFKEARBEARBeDRksBIEQRAEQRAeDRmsBEEQBEEQhEdDBitBEARBEATh0ZDBShAEQRAEQXg0ZLASBEEQBEEQHg0ZrARBEARBEIRHQwYrQRAEQRAE4dGQwUoQBEEQBEF4NGSwEgRBEARBEB4NGawEQRAEQRCER0MGK0EQBEEQBOHRkMFKEARBEARBeDRksNZgdu/eDaVSiRkzZrh7KB5BXFwcOnbs6O5hEAThJq5duwalUolXXnnF3UMxoby8HEqlEq1bt3b3UGziqd8hUfOp1QarUqkU9G/Dhg3uHnKtYc2aNRbff1hYGJo1a4aJEyfi/Pnz7h6iYC5evGj4LDExMSgqKmLdrqKiAk2bNjVse/z48SoeqSnG4+b778yZM24dM1FzsHWtrVq1yt1DrFHoDdIhQ4a4eyiCmDhxosv1Un8Ovv+q23fo6UjcPQB3MnPmTItlGzduxO3btzFq1CjExMSYrGvevHlVDY14REJCAnr37g0AyM3NxcmTJ7F582Z8//33+O6779CjRw/ex/r1118hErn/HU0ikSA3Nxfbt2/HqFGjLNbv3bsXd+/ehUQiQXl5uRtGaEpoaCjrvbJ8+XIUFRXh1VdfhZeXl8m68PDwqhoeUUtguwYBIDExkfcx6tSpg99//x0BAQHOGlatozZ/hwMHDkS9evVMlp07dw67d+9GixYt0K9fP5N1devWrcLR1XxqtcE6e/Zsi2VHjhzB7du38cwzz6Br165uGBVhTJs2bSx+p9mzZ2P16tV48803cfToUd7HiouLc/bw7CIxMRH//PMP1q9fz2qwrlu3Dj4+PmjXrh0OHDjghhGaEhoaynqvfP755ygqKsK0adMQHBzshpERtQm2a1AoUqkUDRs2dMJoai+1+TscPHgwBg8ebLJs/fr12L17N1q2bOmUa5Tgxv3upmrG008/DaVSiTt37pgsnzRpEpRKJZKSkkyWl5WVISoqCh06dDBZXlFRgTVr1qBHjx6IiopCZGQkunfvjk8//RQVFRWCxnT37l1MmjQJcXFxiIiIQPfu3bF161bO7f/++2/MnTsX3bp1Q1xcHMLCwtCiRQu89tpruH//vsm227ZtsxoHW1paCrVajZiYGBQWFgIACgsLsXz5cnTp0gWxsbGIiIhAs2bN8PTTT2PPnj2CPhsbEydOBAD89ddfKC4uBmAar3vhwgWMHDkS9erVg1KpxLVr1wBwx7AWFRVhyZIl6NSpEyIiIlCnTh0kJSVh06ZNFtvm5+dDqVSiY8eOyMzMxPTp09GkSRMEBQVh/fr1vMYvk8kwcuRIHD9+HJcvXzZZd+/ePfzyyy8YNmwY/Pz8WPffv38/pkyZgsTERERHRyMiIgKdOnXC0qVLUVpaarLtlStXEB0djfr16yM1NdVkXWFhITp06IDAwEDs37+f19jt4caNG5g+fTpatGiBsLAw1KtXDyNHjsTp06cttv3444+hVCqxdu1aHD58GAMGDEB0dDTi4uIwceJEPHz4EABw/vx5PP3006hbty4iIyMxYsQIXL9+3eJ4w4cPh1KpxD///INPP/0U7du3h0qlQpMmTTBz5kzk5OS47HMTVc97770HpVKJb7/9Frt27UJSUpLh+gesx18WFxdj+fLl6Natm0GTe/bsif/+97/QarUm2xpPm2dkZOCVV15Bw4YNERYWho4dO3KGj5WUlOCDDz5Ay5YtoVKp0LJlS/z73/+2uG/5cuXKFUydOhXNmjVDaGgo6tevj9GjR7OGTGVnZ2PhwoXo2LEj6tSpg6ioKLRo0QLPPfec4cV//fr1SEhIAAAcOnTIZHp7yZIlVr9D4yn5zZs3o1u3boiIiEDjxo0xb948w2f89ddf0a9fP0RHRyM2NhaTJk1Cdna2xXgPHTqEl19+Ge3atUOdOnUQERGBjh074oMPPjDovp74+Hhs3rwZANCvXz/DmM1fpIX8xq7g22+/xYABAxATEwOVSoXExEQsXLjQ8Ow0JiYmBjExMSguLsY777yD5s2bIzw8HB06dDB8Vo1Gg+XLlyMxMREqlQrNmjXDJ598YnGs8+fPQ6lU4plnnsGNGzcwduxYg73Qu3dv/Pzzzy7/7M6gVntY7aFHjx7Ys2cPDh48iNGjRxuW//bbbwCA06dPIy8vz2BsnDp1CgUFBejWrZthW61Wi+effx67du1CnTp18Nxzz4FhGPz000+YNWsWDh48iI0bN/Kavr537x769OmDu3fvokuXLkhMTMTdu3cxefJk9OrVi3WfLVu2YMOGDejSpQs6deoEsViMCxcuYO3atYbPFhoaCgAYNGgQwsPD8e233+Kdd96Bt7e3ybF27tyJBw8eYMKECYZ1Y8eOxS+//IJmzZph1KhRUCgUSE1NRXJyMnbv3m1h1DuTixcvIikpCS1btsQzzzyDzMxMyGQyzu2LioowaNAgJCcno3Hjxhg/fjyKiorwww8/4KWXXkJycjKWLl1qsV9BQQH69++PiooKDBgwAAzDICwsjPc4x4wZg1WrVmH9+vV47733DMs3bNiAiooKjBkzBitWrGDdd/HixUhLS0Pbtm3Rv39/FBQU4OjRo3jvvfdw/PhxbNmyBQzDAAAaNGiAjz/+GBMmTMCLL76IH3/8EWKxGAAwY8YM/P3333jttdc4rxVHOXbsGEaOHImCggL06dMHQ4YMQVpaGn788Ufs27cP69atQ//+/S3227dvH2bPno2kpCSMHTsWv/32GzZv3oxr165h8eLFGDRoEDp37ozRo0fjzJkz2Lt3L4YPH47ff/8dEomlrM2fPx9Hjx7FsGHD0LdvX+zfvx+fffYZjh07hl9++cUipIGo3mzZsgX79+9HUlISXnzxRaSnp1vdPjc3F0OGDMGZM2fQqlUrPPPMM9Bqtdi3bx+mT5+O06dPs96PWVlZ6N27N7y9vTF06FAUFxdj+/btmDp1KsRiMUaOHGnYVq/7e/bsQb169TB+/HiUlpbi66+/xoULFwR/xgMHDmD06NEoLS1FUlIS4uLikJqaip07d2Lv3r349ttvDSFTGo0Gw4YNw5kzZ5CYmIjnnnsOUqkUqampOHbsGA4fPozOnTujRYsWmDhxItasWYPY2FiT8Xfq1InXuFatWoVDhw6hb9++6NSpE3bv3o0VK1YgKysLPXv2xNSpU9GvXz+0adMGBw4cwLfffovs7Gx8++23Jsf56KOPcOPGDSQmJqJv374oKirCsWPH8MEHH+DIkSPYsWOHQcumTJmCjRs34uLFi3j22WcRHR0NACbPUHt/Y2eg1WoxadIkbN68GbGxsRg6dCh8fX1x4sQJLFq0CPv27cOPP/4IhUJhsd+oUaNw8+ZN9O3bF8XFxdiyZQsmTpwILy8v7Nq1C4cPH0bv3r3RvXt3bNu2DfPnz0dAQADGjRtnMY709HT06dMHMTExGDNmDNLT0w2haStWrMBzzz3nks/vLMhgFUj37t0BAIcPHzYYrJcvX0Zqaip69uyJAwcO4OjRo+jbty8A3Vui8X6A7i12165daNOmDXbu3Gkw9N566y0MGTIEu3fvxldffYUXX3zR5njmzZuHu3fv4v/+7//w9ttvG5a/+OKLFvE0esaOHYt//etfFobcjh07MGbMGCxbtsxgREmlUjz//PNYvHgxtmzZgueff95kn6+++goA8MILLwCo9BB27NgRu3btsjC6MzMzbX4mW3zxxRcAdG/V5jf40aNHMW/ePLz++uu8jrV06VIkJyejf//++Prrrw0COGfOHDz++OP44osvkJSUZIij1XPr1i307dsX69atg1wuF/wZGjVqhA4dOuCbb77BW2+9BZlMBq1Wi6+//hrx8fFW4/I+/fRTi9gorVaLWbNm4bPPPsOePXsM1x8AjBgxAocPH8bXX3+NhQsXYu7cudi0aRM2bNiADh06YM6cOYLHz4f8/HyMHTsWAHDw4EGTGPBr166hR48eeOWVV9CjRw+LF6Hdu3fjhx9+QOfOnQHosqgff/xxJCcnY+jQoVi+fDmGDx9u2H7UqFH4+eef8cMPP+CJJ56wGMvRo0dx+PBhw/f29ttv44UXXsD27dvx8ccfu+w7IJzHwoULLZapVCqD9hizb98+bNu2jXeM+8yZM3HmzBm8++67Jp7D4uJiPPvss/j6668xaNAg9OnTx2S/8+fPY/z48Vi8eLFB6yZNmoSuXbti+fLlJgbfpk2bsGfPHiQmJmLnzp0G7ZozZw569uzJa5x6srKyMG7cOCgUChw4cMBkiv7ixYvo3bs3pkyZgrNnz0Imk+H8+fM4c+YMBg0ahK+//trkWFqtFllZWQCAVq1awd/fH2vWrEHdunXtmuI2v9dmzZqF1q1bY+PGjfjpp5+we/dutGrVCoDO49ytWzfs2bMHFy9eRHx8vOE4y5YtQ2xsrOHlW89bb72F5cuX48cffzQkNb388ss4f/48Ll68iNGjR7POpNn7GzuDzz//HJs3b8bIkSOxfPlyw7NXq9Vi3rx5+M9//oNly5ZZxGnn5eUB0H2n+pfqwYMHY8SIEZg6dSpiY2Nx7NgxKJVKALrncOfOnfHxxx+zGqzJycmGZ7yeqVOnolevXpg1axb69+/v0eFdFBIgkPj4eKhUKoMhClQapXPmzIFUKsXBgwcN6w4fPgyxWIwuXboYlv3vf/8DACxYsMDkQe3l5WUwFNetW2dzLPn5+di+fTsCAwPxxhtvmKxr3769RayNnujoaFav45AhQxATE2MxPTxu3DhIJBKDcaonJSUFR44cQceOHdGkSRMAlW+0crmc1UMs9GY4ffo0Fi5ciIULF2LOnDl47LHHsHLlSkgkErz77rsW29epUwfTpk3jffz//e9/EIvFeP/99w3GKgAEBQUZjBiu3+L999+3y1jV8/zzzyMzMxO7du0CoPOY3Lx50+KlwBy2QH6GYTB16lQAYJ3eX7x4MZo0aYKPPvoIn3/+Od544w0EBQXhiy++YPVIOoOtW7ciPT0dr7/+ukXCYlxcHMaPH4/MzEzs3bvXYt+hQ4cajFVAl6imN0QbN25sYqwCulAdAJyeqgkTJph8byKRCPPnz4dYLKbqH9WERYsWWfxbu3Yt67YDBw7kbaxmZGRg8+bNSEhIsJjmVigUmDdvHgBYeAABwM/PD++8846J1jVt2hTt2rXDpUuXTCqB6K+zefPmmbxoBwYG8n7B1rNx40ZkZ2dj9uzZFvGk8fHxePbZZ5GammqY+dOPj20mgWEYBAUFCTq/NaZMmWJyr+lD5TQaDQYNGmQwVgHdc0JvdP75558mx6lbt66FsQrojFOAXee4cOQ3dgarVq2Cj48PPv74Y5NnL8MwhuuB69wLFiww+d169+6NwMBA5ObmYvbs2QZjFdD99i1atMCtW7dYw50UCgXmzp1rskx/vRQUFGDbtm2OflSXQh5WO+jWrRu+++47wxvhoUOHEBYWhsTERLRt29ZgwBYUFCA5ORmtW7c2yag8f/48ZDIZ61tgu3bt4OPjg7/++gsVFRUmRpQ5Fy9eRHl5OVq1agUfHx+L9Z07d8b27dstlms0GmzYsAGbNm3CxYsXkZOTA41GY1gfGBhosn1ERAQGDBiAHTt24OzZswbB0Ruwxp5glUqF7t274+DBg+jWrRsGDBiADh06oG3btqxjtMUff/yBP/74A4DO26tSqTB8+HC88sorJsKnp1WrVrwNsPv37yMtLQ316tVjNQL1D7xz585ZrAsMDHQ4iWvYsGGYNWsW1q1bh2HDhmHdunVQKBQmXhk2cnNzMQBHGgAAIABJREFUsXLlSvz888+4du0aCgoKTOKv7t27Z7GPl5cXvvrqK/Tq1QszZswAwzD44osvDFNnruDEiRMAdDHTbN6xS5cuAdC9+JjTokULi2UqlQoAe7UO/bq7d++yjsXY+NVTt25dREVF4datW8jMzPRozwIB1jhHLtq2bct72+TkZEPeANt1qo+9NI83B3QhN2y6FhkZCa1Wi5ycHIOxcf78eYhEIlbdN3Zo8OHkyZMAdC9obGPWx+1fvnwZjz32GOLj49G8eXNs3rwZN2/eRL9+/dC+fXskJCQ49NLNBtu9q68awnbv6teZx9jn5+dj9erV2LVrF65evYr8/HybOseFI7+xo6SlpeHGjRuIjIxkjS8FdPp87do1lJeXmzy/pFKpiddZT3h4OLKysji/z3PnziE1NdWikkPDhg0N4X7GdO7cGV9++aXHl4skg9UO9AbroUOH0LhxYxw5csQwZdy9e3d88MEHSEtLw/nz51FWVmYSDlBcXIySkhJERkZyxqiGhYXh+vXryM/Pt1o6JDc3FwBYL0D9cdiYPn061q9fj6ioKPTp0wcREREG0Vq3bp1hGsKY8ePHY8eOHVi7di2WL1+O4uJifPPNNwgJCbHw5G7cuBGffPIJtm7dahAHuVyOgQMH4t1330VkZCTnZzJnwoQJhmB/PugNFz7ovz+ufUJCQgwlqBw5DxdeXl546qmn8OWXXyI5ORk//fQThg0bZvLGbE5xcTH69u2LixcvolmzZhgxYgSCgoIgkUhQVlaGjz76iDOBo2HDhoiPj0dycjIaNGjgkqkvY/QJUt98843V7QoKCiyWsSWc6YXc2jquMmDW7pFbt24hNzeXDNYahJB4cv11avxyzAbbderv78+6rf56NE6gzcvLQ1BQEKRSqcX2XNenrTHbmonTj1kikWDnzp1YvHgxfvjhB8yfPx8A4OPjgyeeeAILFiywcFTYi733bllZmWFZaWkpBg4ciLNnzyI+Ph5PPPGEQY81Gg2WLFmCkpIS3mNy5Dd2FP25U1NTsWjRIqvbFhQUmDzzvby8WJ1W+mVs36d+nfH3qYfrvtBff2zPOk+CDFY70BugBw8eRPv27ZGdnW1YpjdYDx06ZPDMGSdcKRQKyOVyZGRkQKPRsBqt6enpEIvF8PX1tToOvVg+ePCAdT1bosHNmzexfv16tG7dGj/99JPFFBFXpnvXrl3RpEkTbN26Fe+99x5++uknZGVlYdq0aRbhBT4+PnjzzTfx5ptv4s6dOzh+/Dg2bdqErVu34vLlyzh8+DDrVI8zEHJc/feXlpbGuj4jIwPl5eWsDyVnjX/MmDH44osvMGbMGJSVlWHMmDFWt9+6dSsuXryI8ePHWySDXb16FR999BHnvkuWLEFycjKCg4ORkpKCRYsWubQMi1549+zZg/bt27vsPHywdY9wGR5E9cQeHZgyZQref/99Vw0Jfn5+yMrKQllZmYXRynV9cqEf85EjR9CsWbP/Z+/M45sqs///uUn3HUppoYUCUlZBBFkEBcQFBUcWcXBU8MeIzKDOiNsg+FVxRRBEnUFGQFQUx0FABWVELMha9q3sZWmhhe5N26RJs93fHyFpcvPc3OcmNxt93q+XL2xylyfJveee5zyfcw7VPikpKXj33Xfx7rvvorCwELt27cJXX32Fr776CiUlJSG1HLxhwwYcOXIEkyZNckuEKi4ulhXEAAL3G3s696233hr0bHyx5EP79RfqdpBpWL2gffv26NixI3bv3o3c3FwATcvH/fv3R0JCArZt24Zt27YhJibG7WHdu3dvGI1Gx5KpM/aqAjfeeKNHOQBg055ERETgyJEjxJkhqUapvfTP3Xff7easnj9/3uMyyxNPPAGdTof//ve/+Pzzz8FxHFHY7UxWVhYeeughrFmzBj179kR+fj6Kioo87hMoMjIykJGRgUuXLqGwsNDtfbu0gyQ9UIpevXqhb9++KCkpQefOnYlL187Yfz+SPtlTTdodO3Zg/vz56NKlC/bs2YMuXbrg/fffx/bt2337AB6wJ44Fu1MXQP5uCgsLUVJSgszMTBZdbcbccsst4DjO79dp7969YbVaiefZuXOnrGMNGDAAgPf3VocOHfDoo49iw4YNaNOmDbZu3QqtVgugSe8qt7yiktglDXLsnKdxB+o3JpGZmYmsrCzk5+c7vuNgcfbsWeLkyP6dkuQcoQRzWL1k+PDhqK+vx9KlS9G5c2eHFjAiIgKDBw/Gpk2bcOLECQwaNMgtk91eOuL11193EeUbDAaH+Fsq8QYAEhISMHbsWNTU1LhF2/bu3Yv169e77WPv3rVr1y4XPVBtba1kstLEiRORmJiIhQsXYu/evRgxYoSb9vPq1avEtpx6vd4hNfBUZirQPPbYY7BYLHj11VddDF1NTY1jJk7zW/jCJ598gq+//totqY2E/fezJ1PYKSgowDvvvEPcp7KyEtOmTUNkZCRWrFiBtLQ0fP7554iOjsa0adNQWVnp+4cgMHHiRLRq1QqLFi0Sfcjs27ePWINQaZYtW+YyKbFarXjjjTdgsVjwyCOP+P38jNAlIyMDDz30EA4fPoz58+cTZSXFxcVErbUcHn30UQC2WrHOdURramqwcOFCWceaNGkSkpKSMG/ePBw4cMDtfZ7nsWvXLsey8MWLF3H69Gm37bRaLRoaGhAZGelYmrcnYAlrjQcSMTt38eJFvPnmm8R97JNO0rgD9RuL8dRTT0Gr1eLZZ58lLrtrNJqAtLM2GAwuZRQBWy7MqlWrEBcXh3Hjxvl9DL7AJAFeMmzYMHz++eeoqKhw6xc8bNgw/Prrr47/F/LYY4/hl19+wcaNGzFo0CCMGjXKUYe1sLAQI0eOlIxc2nnrrbeQl5eHRYsWYf/+/RgwYABKSkrw/fffY+TIkW5LEJ06dcLIkSOxadMmDBs2DEOHDkVNTQ22bNmCli1bokuXLqKJK4mJiZg4caKjrBSpnExhYSHuu+8+dO/eHb169UJmZia0Wi1+/fVXXLp0CQ8//LAsDau/eeGFF/D7779jw4YNuP3223HXXXc56rCWlZVh6tSpbiWtlKZbt27o1q0b1bYPPPAA5s+fjwULFuDo0aPo3r07Ll26hF9++QX33Xef27Kevf7f1atXsWjRIsfyYc+ePTF37lzMmDEDf/nLX1xqtypFUlISvv76azz88MMYPXo0hgwZgh49eiA6OholJSU4dOgQioqKcObMGbeyVkpz2223YejQoRg3bhxatGiB3Nxc5Ofno1evXnjuuef8em5G6LNgwQJcvHgR7777Lr799lvceuutaN26NUpLS3Hu3DkcOHAA8+bNQ05OjtfnePjhh/HDDz9g06ZNuPXWWzFq1CiYTCasX78eN998M3GVR4zU1FSsXLkSkyZNwt13342hQ4eiW7duiIiIQHFxMQ4ePIji4mIUFxcjMjISx44dw+OPP46bbroJPXr0QJs2bVBdXY1NmzY5ghX2wEpSUhL69++P/fv3409/+hN69+6NiIgI3HbbbcSEMX8watQoZGdn4+OPP8aJEydw44034vLly9i0aRNGjhxJlC+MGDECixcvxpw5c3D8+HEkJydDpVI5KjAE4jcWY/r06cjPz8d//vMf/P777xgxYgQyMzNRU1ODoqIi7N69Gw8++CCWLFmi+LmdueWWW/DLL7/grrvuwu23346Kigp8//33MBgM+Oc//4lWrVr59fy+whxWLxk6dCg4jgPP825OqfPfJIdVpVLhq6++wvLly/HNN9/giy++AADk5ORg7ty5ePLJJ6l73rdp0wa//vor3njjDWzevBkHDhxA165dsWTJEsTHxxM1M8uXL8f8+fOxYcMGLFu2DGlpaRgzZgxmzZrl5nwLeeyxx7B8+XJkZma61Pq006VLF8ycORM7d+7Ejh07UFVVhZSUFNxwww14+eWX8cc//pHqcwWK2NhYrF+/HosXL8a6deuwdOlSqNVq9OzZE3PmzCG2Tg0mKSkp+OmnnzBnzhzk5eVh+/bt6NSpE1577TU8+uijboZ80aJFyM3NxdixY90mQfaC/GvXrsWHH37oF8dt0KBB2L17N/71r3/ht99+c9S6TU9PR58+fTB79uyALMe//vrr2Lp1K1asWIHCwkK0bNkS06ZNw+zZs/3uLDNCn6SkJGzcuBFffvkl1qxZgw0bNsBgMCAtLQ3Z2dmYM2eOaJlAWjiOw8qVK/HBBx/gP//5D5YtW4aMjAxMmjQJzz33nOyJ/PDhw7Fz504sXrwYubm52Lt3LyIiIpCeno6BAwe6NHrp168fnn/+eezatQu5ubmoqalBq1at0LVrV7z33ntudn/ZsmWYPXs29uzZg02bNsFqteKVV14JmMOamJiIn376CW+88QZ27tyJXbt2oUOHDnj55Zcxbdo0osN65513Yu7cufjyyy+xbNkyNDY2Qq1WOxzWQPzGYnAchyVLlmDUqFFYuXIltm7ditraWrRo0QKZmZl4+umnA/JsbN26NZYvX47XX38dX3zxBfR6PXr27IkXXniB2MAl1OA0Go3/+5Exrhu++OILzJgxA7NmzXIrcsxghBoPPvggcnNzsW/fvmbb/5zBYDRvjh07hqFDh2LUqFH45ptvgj0cr2EaVgY1RqMRixcvRlRUlKODEYPBYDAYDIa/YZIAhiTbtm1DXl4etm3bhoKCAjzzzDOK1CFlMBgMBoPBoIE5rAxJNm/ejH/9619o2bIlnnzySUclAwaDwWAwGIxAwDSsDAaDwWAwGIyQhmlYGQwGg8FgMBghDXNYGQwGg8FgMBghDXNYGQwGg8FgMBghTbN2WP3Vhs0fsLH6h3AaK8DG60/CaayhTDh9j2ys/iGcxgqE13jDaayAsuNt1g4rg8FgMBgMBiP0YQ4rg8FgMBgMBiOkYQ4rg8FgMBgMBiOkYQ4rg8FgMBgMBiOkYQ4rg8FgMBgMBiOkYQ4rI+yoNVoxcXMlsr66gr9ur4bRwpq1MRgMRqiiN/P48+/VaPf1FUzeUgWdyUq9b02jFeM3VaLd11fw7K4amK08KvQW/OF/FWj39RW8lKeBlWfPgOYAc1gZYcfq8w3YVNwIrZnHt+f1yC0xBHtIDAaDwRDhpyI91l3Uo97EY32RAesu6qn3/fKMDluuNKLexOPLsw3YcbURy0/rsKPUiHoTj2WnddhbbvTj6BmhAnNYGWHHS3tqPf7NYDAYjNDh6Z01Ln//bZeGet85B+tc/n7tQB3mHal3ee3dQ67bMK5PmMPKCHvMVrYcxGAwGKEKx/n3+DtKjSioNfn3JIygwxxWRthjZv4qg8FghCx+9lcBAO8Loq6M64+gOqz19fV4+eWXceONNyIjIwP33HMPDh065Hif53nMnTsX3bp1Q0ZGBkaPHo1Tp04FccSMUIRFWBkMBiN04RR0WcWOtPoCvS6WEZ4E1WH9+9//ji1btmDJkiXYvXs37rjjDowdOxZXrlwBAHz00UdYvHgx5s2bhy1btiAtLQ3jxo1DfT2bSTGaYEUCGAwGI/jkV5sw5IcyjN4Xg+/ONzheV1IScKyaLf03V4LmsOr1eqxfvx6vv/46br/9dnTq1AmzZs1Cx44dsWLFCvA8jyVLlmDGjBkYM2YMevTogSVLlkCr1WLNmjXBGjYjBDHTV0gJGWqNVkzdVo2+a0qx4Gg9eFaWhcFghDlzDtTiRI0Z5UYVns/TQM/0WgwFCZrDajabYbFYEBMT4/J6bGws8vLyUFRUhLKyMowYMcLlvcGDB2Pv3r2BHi4jhDGHobP3xRkd1lzQ40K9BW8fqsPRKhY1YDAY4U1uSaPj/+tNPA5U2MpNBULDyrj+iQjWiRMTEzFgwAAsWLAA3bt3R3p6OtasWYN9+/ahU6dOKCsrAwCkpaW57JeWloarV6+KHregoEDWOORuH0zYWO3EufxlsfI+nS8Y3+vrB1w/w9t5VzG3G10twXC6DoDwGi/tWHNycvw8EgYj/FFd81SZw8pQgqA5rADw6aef4umnn0aPHj2gVqtx0003YcKECTh69KhjG04gfuF53u01Z+Q8SAoKCsLmwRMqYy2sN+NUjQm3pkcjJZocoPf7WHeWuPxpBef1+YL2vQo+Q2RsAnJyUiV3C5XrgJZwGm84jZXBCAfUdoeVeawMBQiqw9qxY0ds3LgROp0O9fX1yMjIwJQpU5CdnY309HQAQHl5ObKyshz7VFZWukVdGYHhSKURo/9XCZ2ZR1a8GjvHtBZ1Wv1JBHf9lbKKi2AWncFghC8WQrUWZtUYShISdVjj4+ORkZEBjUaD3NxcjBo1yuG0bt261bGdwWBAXl4eBg4cGMTRNl9m76uF7pqnWKyzYOkpbVDGERESV62yxDCHlcFghDF6QrkW47WE2HCzbgYzjxm7anDTd6V4aY8GRlaKJiQI6qM/NzcXmzdvRmFhIbZu3Yr7778fOTk5ePTRR8FxHKZPn44PP/wQ69evx8mTJ/HUU08hPj4eEyZMCOawmy27y1w1lj8VGYIyDvV1uL4Up77+PhODwWg+kCoCGO1R1zAzbxuK9PjibAOKtBYsO6XD5uLgPOsYrgRVElBXV4c33ngDV65cQYsWLfDAAw/g//7v/xAZGQkAePbZZ6HX6/HSSy9Bo9GgX79+WLduHRITE4M5bMY1glVNSh3CEdafivRYclKLnKQIvNk/GUlR7oO1EqoasAgrg8EIZxpIDuu1yGS4Wbcnt9e4/P23XRqMzo4N0mgYdoLqsI4bNw7jxo0TfZ/jOMyaNQuzZs0K4KgYtJAcr0AQwXEAQm+JpkJvwZTfq2GyArtKjWgZo8Jr/ZLdtqszuo/dEoaluRgMBsOOJ0mAJ34rNuC1A7VIilThwyEp6JYS6dM4VpzW4Z/H69E+IQKzbk7EoPRon44HANWNVpyqMSFazeFvu2pQqbdidt8kjOnAnNhAEsKxKkaoEywfK1Q1rEtP6WByMtAfHCNrfDUEK95o8deoGAwGw/+QJAGNEhFWs5XHX3fU4GSNGXvKjXhlX61PY7jaYMHzeRpcrLdg29VG3LuxEk/vrEGlwXcD+8q+Wry2vxa7So04U2vGMztrUG8Kw641YUyIPvoZ4QAhKdRBhd6CI5VGmDxt5CWhunpe2kBnFDWNJIeVRVgZDEb4QpQEXLP/YmkHx6tNqDQ02UPnxgPeUFBrdnttVUEDbllbhi/O6HxaFdxypRE/XWrSstabePzGtK0BhTmsDK8Rm1ueqFfhlnVlGL6hAqM2VsCssNOqIli/UGhtqqOstUWKsLIsVAaDEW78VmzA0B/LMeaXShyvdu/WZ7w2h+dEYqxKxzPEHFKNkceM3RqM/LkCZZSBBRpKG1iENZAwh5XhNWLGYeGFSNRe02nurzBhQ5He72MJhZUZLeUgagkaVgNzWBkMRhhhsvJ4cns1jlWbsO1qI2budV/OD/TKkdTp9leY8M7hOsXOV0VYLWP4D+awMrxGzDjk16td/la6/BUpYhsKDl+9iW4MRK0Xs3sMBiOM2F9uRE2jZ5tnl4QFSsVlEdjR1rEqt6YsPxcZiE0OvKHawAx3IGEOK8NraO95lcLWihTIDAWHVUvpsJKiDkwSwGAwwgkak+VIuiI8A0xWHq8d8C3Jyn1MroO6uVUU9o5rjaSopgFUNVpxqNJdvuANpXqWLRtImMPK8Bpah1XpOv8mghQhFBxWHaUkgOSwsqQrBoMRTtDY9UYPna5+LjJgZ6mR8I73CM2omgPaJUTgzrYxLq9vuqzMqt9lLXNYAwlzWBleQ5vnpPRykHDZBwCKQ8BwaCmTrhoJnj5zWBkMRjhBY9dNHiKsf95WreyAQHZYAeCedgKHVaHs/hqm5QoozGFleI2Vsng/KavfF0ilso5UyVvi0TRaUa1AbT47lQYLyvW0EVbCa8zuMRiMEOFQhRGjNlZg/KZKnKg24e1DdRixoRzvHq5z5BDQWPVGD9v6oeKhmzbV3sb77qxolzHkV5twRee7/WeBhsAS1E5XjPCG9l4NhIb1SBX90tK35xrw9101sPDAjI4ReDXHt/G8f6QO7xyup96eaVgZDEaowvM8nthWjYv1Noduy4/ljvcOVZrQt1Uk7m1H1+HJGOCFL7EIa6sYNW5Ji8T+iqbAxuZiAx7vGu/T+UJBitacYBFWhtdQJ10pek6eGNcl1QAU46mdNTBabcZt4YUon5ob1Bqtos6qMDvVDsk5ZYaPwWCEAtWNVoezSuKpHRoAdBpWoz/CqB4Qc1gB4J4s5WUBzG4HFuawMryGWsOqYIRVLK9JR5mhD7g72rU0Da9FOKtx76xiJ1pNfp1k5FiElcFghAN1Muzl1wUNSPm8BGWUcikAuPfnCjy9s4bYEVAKYZUA59U9oY5125VGGCjzDsQwWd1lCAz/wRxWhtcIjYMYSioCxKKhtP4eybiYfdCPemr1JzYmUtTBrvUq11vwwbF6zD1cp2hHFgaDwaBByh7aAxD+qq26p9yIVQUN+CifXmZlxy3C6uSx9m4ZiTZxTS6PzsxjV5lvrWABFmUNJMxhZXgNrZ+npIZVzJjS9ogmZej7YnA87Sk2VmLSlcVWamXwD+V482Ad5h2px//7vTokWs4yGIzmg1TQkRP86y8W5Wtl7yO0uc6SAI7jcLdQFqBAeSvmsAYO5rAyvIbWl1KySoBZ5KS0qzKkJABfDI6n1TFSRy6AnHTVYOYx8bcqVDp1TskrM+KozOoHDAaDIYTneXx+WodRGysw50AtLmvNmLqtGmM3VSJPEGWU0vTbAxCh6KYJV6/UgkePUMf6a7HB56AAqXMhwz+wKgEMr6FuHKDgOcU0rLQ+J8k59UXH5MlYib0lpxTKD4V69GkVJXdYDAaD4eBYtQnP5dmSpXaXGfGhU/TyWJUJZx7OQOQ1T5RU59oZewAiFKWbQmdbLQiWDG8bjShVU6ChsN6CC3W+Sa9IK2YM/8AirAyvodawKioJ8E3DSnIWfYmwNngQfFl5slRBTubs9xf1TBbAYDB84s2DdaLvVTdasau0Kcoqtoplx27OQ3ElXGiOhXK0hEgV+qW5BgAuacUTZ2nQh+IXcZ3CHFaG1wQjwirmH9I6zyRn0TeH1fO+pPF66leQnaB2qS5QpLUwWQCDwfAJqY5MzitXUh2m7U5gKEZYpSQBAJAgKDdI2VFbFNY8IHAwh5XhNdR1WBUta+WbhpXkLPrisErpl0jRCrESVhNviMWOMa1xZ6arzuqHQr3X42MwGAyS4+aM89tiq1jCbWkTXQOJ0PkUSgIAIELwQPK1ViyLsAYO5rAyvIY0MSUZMa2Jx79PavHtuQZYeR5Wnsd/zzfg3ye1qJc5vRXzD2ltBrFovxcrQltLDFh0rB6nPNRhBcgRVlKlghXDWuDToS2RFKXCuA6uXWSYLIDBYPgCyXFzxtmHk7Kl9kOFYjdpobNNctSjBPWxfSlrCPiWA8GQB0u6YlBBcpisvO11zskYkvzPrwsaHP9/RmNb3raXLPnufANy/9CaehziEVbKslYKaFg3FOkxaUs11bakQwvHsGNMa/RqGen4+972MYhWN4n57bIAlnzFYDAq9Ba8sr8WZzRm9GwRibf7J6FljEiXkmtI5RE4O6y0VQKkkrOCgVuElRCSi1Q4wsrKWgUO5rAyqBCbRJqsrjNWqZtfWFvvYKUJZzUmdEmJFNlDMA4fqwQo4bAuPUlfH5Bk/IVjiBYY1cRIFe7MjMHGS001Alm1AAaDAQDP7dbgp2u24WiVCWYrj6XDWnrcR5YkQLIO67UqASFY2Er4/CGVVBQ6rL605gaYwxpImCSAQYWYoyg0EN4sr0glBLge38cqAQokXe0oNVJvS5QECHS0UYSnyViBLOCHQiYLYDAYwP8Exe43l0gXv5euhd30vlSr0VCOsNJIAiIFXo+vSVfMYQ0czGFlUCFW6uREtWsGu1hCkSeipab/TphEDm+XJ0hBqpnnTw0SMelKYFRjCJ//3nYxLtUCCutZtQAGg+E+OafR4EuZWOegI22nqxD0VwlJV+7bRCkdYSV8YRV6C57ZWYMpW6txVsPstlIEzWG1WCx4++230bt3b6Snp6N37954++23YTY33X08z2Pu3Lno1q0bMjIyMHr0aJw6dSpYQ27WiM2mX93vWt/Pm5ufFGEUw+TBIaY5NTHpSqaTnRnnWS/mDOl7E56P5LAnRancqgX8dMn3NoIMBqP5IcdhlbLhjqSrEAwsupe1IlUJEO7j+rfclSzS8+PZ3Rp8XdCA7wv1+FNuFVsdU4igOawffvghli9fjnnz5mHfvn147733sGzZMnzwwQeObT766CMsXrwY8+bNw5YtW5CWloZx48ahvr4+WMNutogZMWGWvzfBSjn38vk68XACjd9J7HQl02GNpvdXid+b0GmOErkL72/v6rAerKCXIjAYjOYBT6EllSot6FrWSuJY1/4NRFkruY6ecOw0EVahjEDuM4xUKtE5/+B8nQUX61k7LCUImsO6b98+3HvvvbjvvvuQnZ2NUaNG4b777sPBgwcB2C7UJUuWYMaMGRgzZgx69OiBJUuWQKvVYs2aNcEadrNF7CYWzmi9kQTQFv0HgCMelsVpTk1KCpPTC3pvWSMuyDA+Rivwc5EeGy/pYeV58DzvJksQk0TcIujIcrjSKMu5ZzAY1z8GCzB5SxXmH6kTDSxIa1htHK40YvJWzxVQHBrWANgiuTKodRdda1bTaFiFzyy5eRg0kjJfZQcMG0FzWAcNGoSdO3fi7NmzAIDTp09jx44duPvuuwEARUVFKCsrw4gRIxz7xMbGYvDgwdi7d29QxtycEUt2Ei6neCNgl3Mve3JYaWb8JA0rbaeSbwp0GLmxkmpbO8/srMGjW6rxSG41XtpTC5MVLvEQNedeyNpO5+QIl64sGiOPK41K9g1jXG8wqVXzZH2RAe8ersfyUzri+1IRVitsjtvYTZWS9thRJSAAPtinECWeAAAgAElEQVQDv8izt0JUhA/uViVA8DnkOpcs6SpwBM1hnTFjBiZOnIiBAweiVatWGDRoEP70pz9h6tSpAICysjIAQFpamst+aWlpKC8vD/h4mzti96RQU+rNTFLO/V5Q61uEleSc0nYqeWqnhmo7Z5wd7M9O61Ar8PA9JZypOA69U13LfZ3WsjxJhjhMatW8mbWvlvi6lIbVygPHq02oNUrbQi6AEdY6sSxbSsgRVs+SALmfS6o9N6Bse/LmTNDqsK5btw7ffvstli9fjm7duiE/Px8vv/wy2rdvj8mTJzu24wRLGcJC9UIKCgpkjUPu9sEkmGMtbOAAxLq9rjdZXMZ1oU4FIMZtO4/HvnQZSRrp0KyVBxotcaLvF5w/jySJK/pqRQQA16X2qtp6FBRUUYxU/Ny0fH+kCEC04+8Yzurxd+2gjsRuNDmtp7SqsLpmgevzHsvJyfHzSLzDWWoFANnZ2R6lVgCwZMkS5OTkYM2aNZgyZUrQxs5QnkMVRiw5qcWGIs8Jm1LPVWeakq5CP7JI1rC6/m0UrLrJDbroKDQElF8tQ4KgOayvvfYannnmGTz44IMAgJ49e+Ly5ctYtGgRJk+ejPT0dABAeXk5srKyHPtVVla6RV2dkfMgKSgoCNkHj5Bgj9VcYwIOuUe2rZzKZVwVpY3AMXnLOG2zspCTHu1xm+1XG/HKvloA4hHWDh07IVWi40tCXS1wybXwvzo2ATk5qdID3VkivY0E57mWAJqW7ZJjIjz+rsNVDfjmSo3j79NaleR1cFpjwsw9tTBYeLx5SxIGSny3/iTY160cwmmsYgwaNAifffYZzp49iy5dujikVs899xwAaakVc1ivHxrMVvzhl0roKCKAtrKAdMe1+3vhsBJOrhLguayVXFmbzscoMIOeoDmsDQ0NUKtdnQu1Wg2r1Xa1ZGdnIz09HVu3bkXfvn0BAAaDAXl5eXjzzTcDPt7mDm3SlT8kAWYrjyd+r0aFwbMl8bZKAK2GVQl2lja6/J0gzAAQ0IcgCZCKhryQp8Gua80Nntxeg6MT0qmjJ4zwZsaMGdBqtRg4cCDUajXMZjNefPFFKqnV1atXRY/LVq5CA9tYPa/02D/P96Vq6Mx0k9VLJSXXVqekV8csZhMKCgpQWqaG82pRKFJZUYYCtWsItabKdZWtskYDpDZ9b1cM5NVEMSrqdCgoECaquf5GhYVFQJxyz5lwumYB5Vauguaw3nvvvfjwww+RnZ2Nbt264dixY1i8eDEefvhhADYpwPTp07Fw4ULk5OSgc+fOWLBgAeLj4zFhwoRgDbvZItb9xGhxXU7yJulKqmPKyRqTpLMKUFYJICRdyakS4CsX6lwHkBDp2ZG0J15pr42x1szhktaC7ETxW3eXUyeuS1oLrjRYkRkvoxYXI2zxl9SKrVwFn4KCAnTu3BnYecXjdvbPE22oB1DncVs7bdpkIjmKA45Kr44VG1RYcDX1mr3VS20eVNqkpyMnJ97ltSxeB5xvykeIS0wGUImcnBxsu9KId87UAaAvIchHxiInp73ri4LVuA4dspGTTNd+XIpwumYBZccbNId1/vz5eOedd/DCCy+gsrIS6enpePzxx/GPf/zDsc2zzz4LvV6Pl156CRqNBv369cO6deuQmJgYrGE3W8QcUR42R9GezO5NhFUpLRTNqUnRVBrRvFIIE7ycqwCQUHEceqVGIq+syYAeqTKJOqykag5SGcKM6wd/Sa0YoYGcxSA56ZlW8LDw9Ibiv+dD21G1Q9KwCk2ufZWwWGvG2E2VFFVtXdF604+c4RVepxzrdDrH8r03JCYm4r333sPx48dRWlqKo0eP4rXXXkNMTNOSBMdxmDVrFs6cOYOysjJs3LgRPXr08PqcDO8Ra80KuMoChGWuaJAywrQ1BGnquRKrBCjksMZFcBjZTl7CmZQkAABubuU6Mz9SKT771xL0VKwGYPjgq12VI7WyY5daDRw40OvzMgKDHIdVjgrIyodmq1VfURNm68LOinZ/c1G+VrazCjANayCR5bDm5+dj4sSJyMzMRPv27bFjxw4AQFVVFSZNmoRdu3b5ZZCM4ONpEum8zC5Wr9UTNBpWGqgirD42DvBEfAQHiRV+N6QkAQDQJ9W1qoGnWrRaQijcm2YOjMChpF21S602bdqEoqIibNiwAYsXL8b9998PoElq9eGHH2L9+vU4efIknnrqKSa1ClH0Zh7vHKrDs7tqUNjAyWqyQjvRB2w2WEqaFY7QlLWyB1xKdN51oxImtZFWDHdcNeKZnTX47LTW8f6WEgOe2VmDlWd1rHUrJdSSgEOHDmH06NFo2bIl7r//fnz33XeO91JTU1FRUYEvv/wSQ4YM8ctAGcHFk6E0+Rxh9Xyz0iZF0RhcUuOABoUsdWwEJ9oEQAw6h1UQYa0yimoOtQTn25vfhBEYlLarTGp1ffHa/losO22rKvK/qGjs60a/r1TtVWesPF2L13CDptOVfY4voc4SRRhhJQVOns+zaWa/LgDiIlS4JS0S43+tuvZaA5IiVRjbkT7Rq7lC7bC++eabyM7ORm5uLgwGA1avXu3y/tChQ12MLSM8+aZAh7UX9RjQOgrP9050zEY9JVM5O0T+qBLQSOlw+UMSsKFIj3lH6nG8WrpFYHwE52YMpaCRBAgTr2oaebfEqzqjFW8fqsP3F921ZaQI68qzOvxwUY/BGdF4rlcCcemM4X+Utqt2qdV7770nuo1dajVr1iyvx80IDHZnFQDKjSpsKvZcT9UZObc0z4dHmSq5kKLMbp2urj2z5AYb7OgtPCxW3mFDpR6B03fU4P72rtKxKb9XY2zHTK/O35ygdlgPHDiAWbNmIT4+Ho2NjW7vZ2VlOUqmMMKTY1VGRzen3JJGtIlTY3IXW4alp2V5ZyfVG/251A1Ou6Ttbacrg8W2jCM0bpe1ZkzeUk0dd4iN4GRFNQAgkSLCSpN4Ne9IPZaKtGUUlh7bV96Iv++y/c5brjSiXYIaE2/wvSkCQz7MrjYvdCYrPszXos5oxd97JRKrd2y/2oh1FxrQNy3K7b3CerPba2LI8b/+e74BU7rGS28YZtA0DrAHY+QGG5zRmXkkRdG3rL0g+B2vw7mCX5D1E0VGipdlKC8vR3R0aNdkY3hmtSDz0+7UABIRVouzJMCLCKvEPrSSAJpTi42PVCngizM6WYbEG0lAPKXoVSgLOFrlmni1+IRrMwRnhJKAvwlazC7xsC/D/zC72nx4aU8t3j9aj09P6TBuU6WbdrGw3pap/sXZBhf7a6eSoryfHVLRfDH+d9mAXy7TR2/DBWKVAJEIq9xggzPOOlYrzVODeaheQe2w9u7dG5s3bya+ZzabsXbtWvTv31+xgTECT40HsaOnpX5/SwJonWAxScDVBosjMmEQ0dVvvdIInckKrcmKsxoTjBYepXp54eI4NSdbB0UjCQCAPq0EiVeV0hIFO8II9Zla19n92Vr6qA1DWZhdbV58c67B8f9na804pXG99z4/rfM48a6S4bDKXeFecYa8QuMNai40PDKS0x6lsCQAsEXO7dA8rkLj2wk/qB3W5557Drm5uXjxxRdx9uxZAEB1dTV27NiBcePGoaCgwNH+jxGedCLU9ixtsHl4nhJ3fJUESDusdMchGYpVBTr0Wl2KPmvK8O7hOlF5waQt1cj8+iqyvr6KAd+X4+6fK2RXPIiN4Nz0UVJI1WG1c7Mgwnr4WuIVDc4OP2mfHi2CVo652cPsavNGWNWjWCJTXU6ENZiydLnVUvyFmuDhKJ10BbiWE6R5bLBKg95B/aS6++678fHHH2P27NlYsWIFAOCJJ54AAMTHx+OTTz7Brbfe6p9RMgIC6R46UWNCRpzac4TVV0mAUlUCCJu9mFfraCs7/0g9UqPp5mhHq0xUiVbOxEVwRAPpCdoIq6fEK2lJRdP/X21wf+BlxTOHNVgwu9q8Ed66Uh3pKsWWiAjIkQQoTZQKkOFb+w2aslamaw8OucEGZ1wkASzC6jdkPakee+wxPPDAA9i8eTPOnz8Pq9WKjh07YuTIkUhJSfHXGBkBguSU2meOnhKfXCUB8s8rGWGldlgJFQAE+1bRlhygGJeQuAgVEiLkeaw0SVeA58SrOonC1c6/q44QAvemdi5DOZhdbb4Ib72MOCmHlUVY5UCuEuD6t918yg02ONPg4rBK21Nmcb2DymE1GAz49NNP0adPHwwbNszR9o9xfUFyWO3ODK0kwORFbRQpf4m+rJXsUytKbASH5Gj/JF0BtsQrZ4f1aJURYzrEQiPxBa29oMeETrYqAEZCgMabqDjDd5hdZQjvXGEGuxBZDqv84ShGlIoHEHyvlVglQPCiPSAS4UNE2jlYQvMLsT4B3kF1TcfExGDu3LkoKiry93gYQYTkzNgjpp6TrpwcVi/uRClJAG2EVTjEQLckjY3gkCL1xBFAKwkAxBOvNBIi3/9dNmB/uc3RJX0nrLFAcGB2lSE0fVIWS45JC6IiIGQirMQqAYLX7ItOvpS1cu49Q/MbsXbZ3kH9E/Xo0YMZ1usc0k1kf82jJMDJ0fVKEiCxD32VANe/DQEOucZFcEih1Mjaoel0ZefGFq6JV0VaW4axVIQVAF47UAuA/PvQaoQZysPsavNGaNqU9GOCGcXzxflTEmKVAGGE1V7WygcNhbMJpfkNhe1cGXRQX1avvvoqPv/8c2zbts2f42EEEZIzY599+lMSoJSGVagdMgTYKCR4E2GVkZqaHOW6rV03JRVhBWzJcwDZ+Wez/eDB7Or1D8/z+OqsDq/vr3V/TxBTVeJWtFcCCebCiU0SEHyokq7sZa18iAqX6Cx462AtPj2pxdEq6WRdYXUIBh3USVefffYZWrZsiXHjxqFjx47o2LEjYmJc24txHIevvvpK8UEyAoMnZ4a6DqsXdkq6SgDtcVz/DnSENSFSXoTVVlWA3krGC8IWDoe1UfpzRnL2FrsESQB94jFDYZhdvf759JQOL+91d1YB9yCBEhbLytsctWDOQ0NFEkAyr2JlrXyRUMzaR/59xZBR7IHhBLXDum/fPnAch7S0NGi1WuTn57ttwwVTNMPwGbK+kXf5V2o//yRdUUoCBMY/8A6rCilRMhxQmVP6WEG4QC8jwhp1LfmYNLFnSVfBg9nV6x8xZxVwt7k0GeZSmKy2jHcljuUtMhea/AYpICAWYWVmMPShdljtRa0Z1y+eJAEmDzNC5yX7UCprpQ+wJCBRZoRVjn4VAKLVAAce/LXsW6PVVsWhTC89Xbd3cSF9l7TfL0N5mF1t3rhFWBW4Fc28LUP/x0K95Lb+InQ0rO6vCcdmtNq+d2YGQ58QuawYweBEtQlvH6zDz0U2w0ZyXEwUEVZ/t2alTQoSnppWSqAUCZEqWZpUORUCAFukLUawS4OZRxmhGYAQu29MjrDKGgaDwVAIob1UwmcyW4G1Fxrwa3GjAkfzjigFWrPOH5js8zFIDquK49xet4DcBZARWlBHWCsqKqi2S0tL83owjMBR1mDBnT+VO7Q0X97Rkqg/tb9GnXTlh05XtFFboV8rbBrgb+IjOVnLt3IjrAAQqwb0Tt+H3syjlCLCas+MJU08mCQgeDC72rwR2jYlbkUzz+OJbTW+H8gHlIiw3pEZjR4pETipMXt9DJLDCtjGZ3Eym2YrkwSEA9QOa5cuXagextXV1T4NiBEYPj+jcxF+T9tejSHp0W7bmWmSrvwsCfA+whr4pCs50Ha5ciZGUJBbb+FR2iDtsNp1W+SkK2apgwWzq+FJrdGKz0/rEBfB4f91jXcrlQQA264YXBp9kHDTsCowNkIzu4CjRNKVmuMQKeZxyjgGiSgV55LjYGaSgLCA2mFduHChm2G1WCwoKirCd999h7Zt22LSpEmKD5DhH45Xu5beaLSIVQmw/UvfmtWLpCvF6rAGW8NqCyt8cGsKns/TSG7f4MX4ogWdG3UmHqU0koBrEQ8mCQgtmF0NTx7+rcrhjB6vMeHjIS1c3t9+tRFjNlVJHsdNEqBEhFWhUGGX5AhMvCEObx2qk72vEklXas7344gVYbFp+p0b3rAIazhA7bD++c9/Fn3vxRdfxB133KHIgBiBIYlgCUgz8yYNq/ixXCUB8sfir7JWUhHWQa2jML1NHR4/GuNxO1rsEdY/d4tHn9RInK01Iy6Cw5KTWmKk5UKd/KWuGEF9w3K9hUr6EMmSrkISZlfDjwt1Zpf7eeXZBjeH9QWKCSvgXlJOicx+pebpI9vFOKqLyCVSgTqsESrOLaNfLmIBWuHjz2zlJJ9DjOCjSNJVcnIyJk+ejH/+859KHI4RAIRF6AFyJNNRJYC2Nasfkq687XQl5chFqTn0SLQiO0HaKidRlKtyNq5906LwcOc4PNAhFjckkeeFLWPkPw1iBXfshXo6p9dui0k6ZTMf3BI4DDLMrvoXo4XHd1cj8O+TWlzRWfBRfj1WntVJTuDOU0w0C2rp7kthNFQZSYAy97Ivq/FKSAIiOCDaV0mAiMMbIXjdHIYR1nqTFf86brtmLeE2eC+hjrBKER0djZKSEqUOx/AzUYQbuZrQ4pOmNatzyStvlpf91elKKsJqr0AVR5HZv/i2Fpi0pUlHeGdmNHJL6LJwu6aQb7OnesRT7e+M0Me9SiEHAJpq2YrVyTVagBjFrAFDKZhd9R/P7KrB6vNRwPlal1qpBbVmvNVfPEP9ik658iNCe6lMWSvfjwGIL6fToETSlVrluySANsJq4oPbGcwb/rjZSZZSbcL8QSlBHpH/USTCevbsWSxbtgw5OTlKHI4RABoIjstlrbshNlld/yXhHAH1Zna/qkCH7843iM4SCX40EeePtKXEgE9OaD1ub5+9x1I4rPe2i8H0HvG4IUmNJ7vF465MehnBE93i8VCnWJemAo93icP4jnHUx7AjlARUG+i+nKNVJhyoMIpGqwNVKcDK8/j+YgNWFUhHspo7zK76l9XnyXVK/3lc63HFoVhBhzWUk65UHAdvfVYZ/VNEieD8JwkQHjfcIqyF9a6ylKWndEEcTeCgjqkMGDCAmM1aW1uL8vJyxMbG4uuvv1Z0cAz/QUpIIvkP8uuwyh+Lxsjjye01yCsz4oPB7rNE+gir7d+VZ3X4+y5pDZkchzVSxWHuwBTMHWj7W8oZdiYuQoVlw1pSb++JWEGEtUpGsdl7fq7AwNZRxPe8kXJ4w+x9tfj3SZtx3VBkwLd3pQbkvKEKs6uhyakaM3q2jER+tQlbSgwYkhGNW9Js984ViqoctLh3uvL9mEpKArztshalgIZVrQKx+oKsY4iMX1h9wGQNLVnU+Voz1lxsgIUHxmTHomfLSAC233Z9qRrnSuuDPMLgQO2w3njjjW4XL8dxSElJQceOHTFx4kS0atWK+sS9evXC5cuX3V6/5557sHr1agDA8uXL8fHHH6OsrAzdunXD3LlzMXjwYOpzMMShzaB3VAmgTLryJVK34oyO6LCKLe2rBP2y7aJ5GmcVaHJYpVqk3tHWvdxXTnJw1s+FEdYqyggrYPuuxMrsBKrJgt1ZBYBfLhtQZbAg1Qst7/WC0naVoQx5ZY2IVgN3/VSORovN1mwenYZ+aVGKSgLcJvjXiyRAgQirCr5LC8Q+g/C4Zp4LmbJWmkYr7vypHBqjbUCfHNdi97jWaJ8QgX/sqcWKc9EAGoI7yCBB/dRdsWKFoifeunUrLE6Ve0tLSzF8+HCMHTsWALBu3Tq8/PLLWLhwIQYNGoTly5fjoYcewp49e9CuXTtFx9Ic0VE7rJ61j4BrBNTX2T3P824PcJITnBGrwp1ZMVhV0HTjWmRGd1tfy2AiVUxw5vV+SW6vjWgbjZzkCEdyxcdDAqMfEnZ+leOweiJYzQNqjTxSlSnSEJYobVcZynC21ox9FUbHRM7KAzN2a7BjTGuUUzTqoMVdEqBAlQDFIqzee51KlLWKVHEYnB6N/4pIN2iIEBmHMIcjlCQBO0obHc4qAGjNPHZebcQjORFYcaZ5LP2LQX1ZvfDCCzh8+LDo+0eOHMELL7xAfeJWrVohPT3d8d/mzZuRmJjocFgXL16MRx55BI8//ji6du2K999/H+np6czAKwRthNUsUxLga01PkqRAGP175eZE7BjT2i1Z6h97a3HTd6XU50q/tr6e4sG6Pt4lDn1auS+jq1Ucfrs/DR8PScEPI1MxuYv8BCpvEAYjK5VyWIMUXgilZbhgoLRdZdAh1YbTaOGxu9R1NSL/Wu1qA8W9Qhud9EenK29kWSRUHLzWsEb4KAmYlBOHKDWHP3WOo6riQoIDECMiKRA6sqHU6apC7/4DBrrNeKhC7bCuWLEC586dE33/woUL+Pzzz70aBM/z+OqrrzBx4kTExcXBaDTiyJEjGDFihMt2I0aMwN69e706B8MVuZIAT0bQ19aszjQS9hcec1qPBKTFqomC+iJC4pgYbeKkHdZ+aWTNJwAkR6kwuUs8hrcNXIgwVvAgUMxhDUCKLCmpTqmHa7jiT7vKEEfKSpl5cW07jfNAu5Ttj8YBStUT9UU+6mvS1UfXVqyi1By2j2nt1THiIzioPHS6csbEK/e9+UqVwf0Cs4KXnGQ1BxQT4tXU1CA62l3rR8PWrVtRVFTk6OhSVVUFi8Xi1j87LS0N5eXlHo9VUFAg69xytw8mSo61piEGNPOVOl0DCgoK0GCMhdh8u1bb4Bib0Sy+HQ2nC84jJdL1NYPgmJcvnke5CqjTRAIQbCwDc/UVIAkw1VUBIDummooyFHChM72NU7vesmImrGu8FWd09Oty5wsvIbrKP96j/dqw2WHXyggFhUWIqAwdQ0x7jwUqc98Xu8oQR+rZb7LyotE5mghrJMehkWJ5XzhRDK0qAUB3kZJ8Uvjeoarpu0/28mDxHoS07hpW8Wvi7sxobKYsYagEVYSyOFaetY4FJBzWvXv3Ys+ePY6/N23ahCtXrrhtp9FosHr1anTv3t2rQXz55Zfo27cvevfu7fK6UMtI0jcKkfMgKSgoCJuSMUqP1XK0FABFD/qYWOTktId13xWIuUcR0bZtAMC827eakW2zOyEzvmkJiOd5mHa6XnM9unSGiuPQSlMLXKHP1hdyS5dsmEovIierNVBITtTKzmyDnA6xXp9DaRLLL0hukxWvxsKhrTB+UyV15DQ9Mws56co7Rs7Xba3RCuRddXk/rW07v5zXGwJlDwJlVxniSN0WZqt4fWYDYXVK+GwS0066n0f5KgFKVfxQcRyGt41Gi2gONY3yjqlE0pWveKr+IixrVaBTYXURWSvra/MCuZBKFVp56brizQGPDuvWrVsxb948ADbnce3atVi7di1x2/bt2+Odd96RPYCKigps3LgRCxYscLyWmpoKtVrtFk2trKx0i7oyvIOmnSfglHRFUYeV53mfl3iFWkqhwxWpapp9+2pH0mPVKIZnSYASBbCVJCnC8+82b2Ay/tIjAYCtmwttMlUgJAGkB2lzNMKBsKsMz0jdFmaeJzo8PM8Ta1hbeVd7ZCvHRBNhFUgClEi6UuiWspe12jw6Dbes87yyKUSJ1qy+4qmGq/C9T4rEpV8xFGUPlUQswhoIGx3qeHRYp0+fjkceeQQ8z6NPnz6YO3cuRo0a5bINx3GIj49Hy5be1ZlctWoVoqOjMX78eMdrUVFR6NOnD7Zu3epIwgJshv6BBx7w6jwMVxpkl7WSTrpSwvcQaliFDk20k6HxJYsVaJqBpwhT753wsW614iRKrNA5P2Rpf2PgWvTTz5C0f7Ra6uuJQNhVhmekHFaTlZywYxRJzrECcE4NiqS0Tf5IupJbLUUMu1VM8GLWHgoTfU9RXjnjI3WF9Cekyi9WBC8xNpTw+PhLTk5GcrKtRd2+ffuQkZGBxMRExU7O8zxWrlyJ8ePHux336aefxl/+8hf069cPAwcOxIoVK1BaWoopU6Yodv7mDHXSlYWXjJzaS14pURpJuNwmjMo5F5L2xV/NiG2yWJ4irCof9Lj+QCrCStNmlkR+tQl/yPav9IEcYfXrKUMSf9tVhjRSkUyLlUc8wasRs5vCS5tWEiAsFxhKkgD1tc/gjUVRotOVr6hlRFg9ER3gMtHkCCtPTEim5UKdGduvNmJA6yj0aOF93kewoVZU+0PbtWPHDly4cAHLli1ze2/8+PGorq7G+++/j7KyMnTv3h2rV69G+/btFR9Hc8Ns5amXF0y8dCa3kUI2QIvQ6RU6NM6+pS+SgO5ON22KB+uqDoFIgTOJEg5rrJdfytFK1xI+hfVmvLKvFjozj9f6JqGvh2oJJFafb8CK0zpkqiLxUQcrEiJVxOV/WmnK9Uq4aOivN2girCRJgNiqhfB4tA6RSbCfEneDcpIA22fwJsCoRB1WX/EURZUTYQ0FDSvPex9hLaw3Y+iP5dCaeUSpgN/uT0PvVHn2PFSQlQJ4/vx5LF26FEePHkVtbS2sVtcvluM4l2QCKYYOHQqNRrwr0dSpUzF16lQ5Q2RQIGep2GTlJSOnRkvTtr4idFCF53aOsPrmsDZd+h4lAd6fwi8kSdyxnjJjPXGkyuTy90t5Gkdm7Pm6ahybkE7dpvGS1oy/bK+59vCNRPeTOrx4UyJxkkRKYGluKG1XGdJIO6w8cfInViHAVk+4aXtah80fZa2UktnYHVVvHNYILvj3dYQHeyVnmV+sWoQ/MFp4YlMfXzSs84/UQ2u2r4IC/9hTi19Gh2cuEPXz+ODBgxg6dChWr16N2NhYnD59GqmpqYiNjcXZs2ehUqnQs2dPf46VoRC0Xa4AW7YsbYTVqMDyrjAK56ZhdXZYfdAWPe5U6D8+gnORCDhzM6FpQDCRigw4P2RHZtFn35fprWhwqofjXMblstaCwnr6H/fDY1qXSNHbh+oAkLul0ZQIup5hdjXwbLvSiG/OeW5taeaBKMJScJ2I1yB8NYI2wmqxSeM2XtJj7YUGRWRVVxqU0dk0Oazy7OzEG2J9TohVAk+yDOrgyxsAACAASURBVFrJBhDYCKtZZMbii4Y1t8Tg8veecnJ77nCAOsL61ltvoVWrVtiyZQsAoHPnzpg5cyaGDRuG7du3Y9KkSY7MV0Zoo5Oxdm+y8m43SpRK2N1KOQ2rlMPqiyQgMZKD1sTj2V4J6OpU7JXjOLzRPxl/21nj+FwqDnitb5LH6Gso4ryM+XzvROyvMKGaoIkicaHOghtbqogFquXMDcQemCQNVnN3WJldDSzLT2nx4p5aye3MVp4Y7RRr1OEuCaAbj4nnMedAHT467n15PiHFWrMix7FLAmhv/b/2iMfNraLwYMdY5OZXKzIGX/A0aZATYQ2kwyr2CPWlrJUcvW6oQ+2wHjx4EM8//zxSU1NRU1MDoKm93dChQ/HYY4/hrbfewubNm/0zUoZiaIXCKQ+YrO6OaHwkB6NTXT67/6tEiSLhuYQBDWfjIec+VHHAxUfaQGfmiYWoJ94QhzHZsbDwPCJUHCw8jzg50/AQwTnpamB6NM4+nIFWX7rX+CRx24/lKHq0jc8ZvmITIlIEvrk7rMyuBhYaZxWw2TTSpVkh4rAKnVtaJ8Fo4RV1VgGgWKd0hJVu+4dvaGpj7YsJyYpXJsvJs4aV/uERSD2umDm08NJ5J2J16sPwMSYK9Ufhed5RYiUuztatxm5gAaBr1644efKkwsNj+IN6GQ6r2epeISBecAfYI7BKOB9uGlZPkgAZS1Vxag4RKs5j15SYCA7xkSpEq7mQdlY9Za3GCsYdoeLQNo7+s/x62UCc0MjpniMmOSFF4Ju7hrU52lUrz+PnIj02XtJf034GBq2MlaWTNSZ8e95dNlCpJzuDws9Baz788fGVcljtppbWzDr7gN4G9TgAi29r4d3OAjwVTImU4RMHUsMqdj3klRnxdYHO874ir8txzi/UmfHf8w24pDWD53n875IePxfJu08L623HKKxXJtLvDHWEtX379rh48SIAIDo6Gu3bt8e2bdswbtw4ALbyLCkpKYoPkKE8OhneBynCKiydZOZtBtsfGlbhuZ1vvpYyluuvJ7fopZuSHLpQZ7okRxCd02d7JWLmXrrI0n/PN6AfoSKAmLaKhFhSH0mD1dwjrM3Rrj6/W4Mvztqcwand4rHgVv9/PivP496NldTbG62A0eh+bYpFWIWv0roI/rj+S5SKsAr+lcI5gOCNwzr75kQMaxONgQp1vvMkCZAVYQ2gw2oRsbO/X5FuDSvI+3NAu2J2qsaEO3+qQIOZR1IUh8Hp0fjlsk3/OrlLHD4eIj2RKKg1Yfj6CujMPBIjOWx/oDXdySmhdliHDx+OH3/8EXPmzAEAPProo5g7dy6uXLkCnueRm5uLp59+WtHBMfyDPEkAQcOq5tx0rCYrWaMol2d3a/DiHg1MVmBoG3fD5eyjZsiIHMpJNAt1ZvRKQJQKOFdnxviOsdhxtRF1Jh7P3phAXBJ6sns8ztWasey05xk6YHPsJ2911589sa0GA9Ki8ErfRKTGeA5PiH3XpABXc3dYm5tdtVh5h7MKAMtP6wLisO4qNeJ4tUl6QwnKRCOsgr8pj1cnwxbTokR5QaApqZU26colwurF+f7RJ0n0Pbq+Ya709ZAwG6plrXwxh8LmFXZonfPXD9Q6gg11Rt7hrALAyrMN+ODWFMlkwlf31znsf72Jx3tH6vBiG6rTU0HtsL7wwgt44IEHYDQaERUVhRdffBFmsxk//PAD1Go1ZsyYgZkzZyo3MobfkOOwmgnlNKJUNtG6c/TTaOUVa7NpN7jbr7rPKp1nuxlxAa7oHCJEqDj8vVdTofnhbWM8bq/iOPyjTyKVw5pbQp7JH6824Xi1CWV6C1bdmerxGA0i1xdLunKnudnVYNXdPVerzPKkWPTS+dLmeR67SukyseuV8i79gFwNq3Mik9J5PklRHGoJEW87w9tGY0rXeDx+bbKdHMXhye7xotvLibBGcDZ5RCAuXV9iPtZrtVr/d9mAltEq3H4t4EPbS+bXYs9RXJqxOTu5APBTkSE4DmtqaipSU5seVCqVCrNnz8bs2bOVGw0jIMg1kjqBAxKl4mwaIKdngMminMPqCefZbkZs83RYvUGpagc/XzJIbiMmOWGSAHeam10lXQNiySJKkuBlfWIhYvpQ54f5rH108hsgtDu9OSQBtA6rkzlWOgOgRbQKtR40Z9/c2RJxESp8f08qjlebMLZjLJI85CvIqRKg5jhEqABLAH4rnyKsPPDYlipH0OGtW5Lwt16JikkavBma0i16qQ6n1+vRtm1bLFq0SNmzM4KC0AGVQpisEKlyv+GNVmXqsErhbIOSQ6H/X5hAiii0Fqk96y2aRitKGywwEK6DE9UmFBCiXKRtmwvN0a6SVtQV6iTqEcUcVq100tW/T0qvZIQDcluzulZwUfZHbSEx4bYnyd6RGYO/9UpEuwTPsTg5ObUqDoj084TKjpiGlYZ95Y0uK2QrC2zSGy+7dbvhzX2qtP6XKsIaGxuLhIQE1u/6OkGOJABwryoQpebcHKCVZ3XITpTVOM0rnI2iv6My1zsqAF+PaInHtsirmUiKiP1yWY8nfq8R1a8O+bGc+HpzrhLQHO0qaRVG+MrO0kY0mHjclRUtqp88VWObAN2RGY1EijCOMFHUW8QuV/uUnlTDOFxRQV5rVhdJgMJjaaFwbSk5fpSKC1yLbl8mbz8Vua5+2QMEtE0spBB2c6NBTiSbBuqfYcyYMfjxxx+vqxuyuSKnvAtp+0gV57aU++7h+oBIApS+AZo7npbNxCDVA3x2l8arxDa51+L1RnOzq6S2oc4P6feP1OH+/1Xij79V4cltNW7bAsBvxQbc/mM5Jm+txogNFV53AFIS+2cIRLRYCe7OlM7ElxthdZEEKGympSKscrksEiknoebklVD0BV+uH7EOVkr5+t5Y6qBIAgBgwoQJqKqqwv333481a9bgwIEDOH78uNt/jNBHK9OxEEZko1Tkji8BcVgFU+OXbqKLTs0dkOyP4YQ9CV5EnkhOR5neO8fzjMYc0FqcoUZzs6skzbLzK+8crnf8/9qLelwhaEanbqt2RDoLas34oVAveV5/O5L2SzgEfGcq/tozQXKbpjqsdDYi2slLVdq9U9ph7SBjNVDNcQFrNeuLLTxRQ66CoVyEVf4+SgeYqH+1e++91/H/eXl5ottVVwe/Jdv1jNnK498ntSjRWfCXHgmybjw78iUBrs6ImC5lPcWDw1eERfNn9EqA3sxj8QktURSu5oDneifiiW7iGaPNGW+0fUpOTLRmHudqzeji1Cq3OdHc7CrJYfX0IDxba0JbQecjjSBbfF+5EX+8Ic7jef0dx7eGmcNK4/5xMt1OtR9Xv5R2WO9tF4OkSI6qrJhaJb8NuDec1ph8cvTF7iOlWrMKj3+gwoh6oxXD2opLdyKDoWEFgIULFzLNYAgw93AdFh6ztfL7/qIex/+YIXsGJXcZVqhhFbsBdlCWcvEF4YwtPlKFtwck4+0ByXg0t8oti/2Hka0c5T0Y7iR4sWYjLE3ka4T0SJWp2Tqszc2ukh1WcW1cOUXkPoqiWIi/I6zWa9NlOQ02ggnNIyNQuk0alHZYU6JVyP1DGtZd1KN7SiRW5pfjt0qyO6SC8hIHErf9UI5X+4nXovUWpZblne384hNavHKtGsaDHWPx2fCWfj23HWqH9c9//rOyZ2Z4hd1ZBYBSvRW/XDbg/uxYWccQ60QkBkkScE9WtGTdNn/gqbtVLGF521Mb0+bGI53j8M25pqLtf+4Wr0iE1demDEeqpCNk1yvNza6Skuzsr5B0vGKF+p2hWXb0u8Nqj7CGiSSbZpLki6+hcI4UcpKVT+jNSY7EzD62ifJ/PKhuVBxH3TzBF8w88PoB9w6GvuIPScArTqXb1l7U450BFmJd9KAlXTlTWVmJU6dOoaHBvdcyI7BcbSAbdJ63dagiPQTkLum6SQJUHKZ09f8SuzAxICNWhdEenHNSRxKWpNXEC70THQ5/VrwaU7vFI14BDatciYmQw5W+dyC6Hrge7KrRwmNPjQqnNe6/qZXnsbnYvY5vpcGKzcUGYo3Tqw0WFNab8VuxATqRlSGaJU9/66Sbkq7CI8JKgy+rubFq4E6KxC5a7syMRsdE/0UfPEUCAyUJUBr7mJWLsIq/VyrihwQt6QoANm/ejMGDB6NLly4YMmQI9u/fDwCoqqrC8OHDsXHjRmVHx5CElLFdrrfgzp8q0HrlFTy2pRpmwZUmNzFb6JBEqoH72suL6nrDs70TcXB8OlYMa4Fv7myJvHHpyIwXN1qxBKsSyLZ6oc4NyRHYN741NtzbCrvGtkbLGLVXujPhhMfXTP/8KtN19aCXy/ViV3mex5hNlfjbiRgM+aEcPwo07VN+r3Zpy2rn1u/L8dDmKvRdW+b23poLetz6fTkmbK7C8A0VxMk2zT0esAhrmFzGNLebr5rUL+9oiTn9ktA+wXdHU8Vx+GVUGl7u458ScJEe6saqOXl1W0MFu7OomIbVw3tipwhahHXLli14+OGHERkZieeff94lcmfv1vLNN98oOjiGNCaChVxV0IBD16JWP18yYKNA12mUab2FDon9ImwR7V9nMFrF4YbkCIzvFIdR7WMldUykB1cMc1hdaBWjxu1topHsw5qdUIfoa4RVa+Zxsa55dhDwh10tLS3FX//6V9xwww1IT0/HwIEDsXPnTsf7PM9j7ty56NatGzIyMjB69GicOnXK58+yt9yIvDKbjt3Cw9EmEwAK6834sZDcJc2uiSbNe8r1Vsf7BbVm/Oecu8NLcyn7Penq2r/h4rDS9C3y1UdLiFRhRu9E/O1G6YoENKTHqfHyzUkY3lb5nARPC00qBK6slZLYk+ZIv6OF4ANI+Zae3Aax70fppCvqa/K9995Dv379sHXrVkyfPt3t/YEDB+Lo0aOKDo4hjYlwFb1x0FUH88p+11aBcusWiiVdeTtz+3hICtVDhiaZwhlShFXpThvXI/e2i5G1vTDpyleHFQAqm2nLK6XtqkajwciRI8HzPFavXo29e/di/vz5SEtLc2zz0UcfYfHixZg3bx62bNmCtLQ0jBs3DvX19R6OLM2xKnFpx4U69y5n3kCSFNDc4/6PsNpOEC4OK80wlQqO0RxnjoxkI3+YdM+SAC4gSVdKY++aRfqtSSuzUh/R0yqYWIKe0lpm6sPl5+djwoQJUKlURMF2RkYGKioqFB3c9ca+8kYsOlaPo1XKZdPT+Aq1gquTdLF6gpR0ZftX3l3cJzUSf+kejz92iqNaxpMbHSUlWLGkK2neGyivRu1H+Vo0mK04rTHh7UN1GP9rpc9jkFsb+HpBabv68ccfIyMjA59++in69euHDh06YNiwYejatSsAW3R1yZIlmDFjBsaMGYMePXpgyZIl0Gq1WLNmjU+fhZT0qDQkx5PGDvlbcWI/vlB+Fc4o5rBKuEIfDU7B33vRR2H9cZl5yj1Vc4GpEqA09skT6YokSWtoI6ykvBixXYMmCYiKioLJJD6DLikpaVYtBuVysMKIezdW4o2DdbjrpwqcJSQleANJEiCkTlC3UBhhHZAW5XF/sTqscnU9vz/QGvMGpSAmwr21Kwm5+tMYYpWAMLQ0AaZDYgRGyFhmyysz4q4NFRi+vhwLjtbL1kSTUCJKG44obVd//vln9OvXD1OmTEHnzp1x2223YenSpY6HTFFREcrKyjBixAjHPrGxsRg8eDD27t3r/QeB5/anSv26pEuNJrEjcElXfj2NYlBpWBVaBk+P8/wDPd41XlYWvj9K4EWqxL8QVQA7XSmJxXFNun82kixQ6pFs34MUW+BBnqwp/filrhXRv39/rF+/Hk8//bTbezqdDqtWrcKQIUMUHdz1xMt7NQ5jZrIC7xyuw5d3pMo6Bkl3QqprGKMGPK2wCi/WeInSRkpLAgC6pQK5szNSRDY6HKfGQUBuaaqTGmWWeO001xatStvVwsJCfPbZZ3jqqacwY8YM5OfnY+bMmQCAadOmoazMltjkLBGw/3316lXR4xYUFEieu6ZKDcB14mPfr7hGBUCe9IREVZ0OgOuySXlZGQo4sWopwEmtCtur1AD8V+u36PJlJGqsKNJzAPyfkGqnZ4IFJ7TylpHGpptxuaQEUr9HYVERoqrsdkG67JzwGrH/3YkHWkfFoNxINvo015Yzf4gHPuViYeRttn1OTqPsYwiJ4MRdoeJLl2A2RkF43QmJVfHQW0PreXP2bAFqNFEQunpnzl9EbbTA5vOx8CQMuHCxEKZYHrZKc67Xw8XCIhiiebfXNXX1QFv63zgnJ8fj+9QO68yZM3H//fdj4sSJeOihhwAAp06dQnFxMT766CNUV1fjpZdeoj1cs2N/hWsUZftV+TVMGwkOKykylRSlgsFDwW2hJECqtJHwtHZn04sSnk3HoJIEyDsmKZqqdFmN6xVhFD3QNNcIq9J21Wq14uabb8brr78OALjppptw4cIFLF++HNOmTXNsJ5Qf8DzvsTan1IMEAC7HGYBTVS6vdbqhM9QqDkXFBuBElcie9HBRMQBcbWlaejpycshl9l7eq8G/T+p8Pq8UmVlZyGkdDavGBBws9/v57Gz4Qyb+fUqHnwr1kpPIni0i8FCnOEzvmYAdVxslf4+s9u2Rk3pt9W1nieRYnK+RgoICl79z25qx/JQOHx3XetyPltwME74914DeqZH4Y6dYn5tvRBZfFH2vY4dsxBXXAFrPq6IZ8RG4WB9aWvxOnTsjqUIDlLkmK2a274BOSa7un3rPFY9LBO2zs9E5ORKaRiuQ5zq5zWqfjTZxKmBPqcvr8QmJAKq8+o1JyIqwfvvtt5gxYwaefPJJAMCsWbMAAO3atcO3336Lnj17KjKo5oCUrmfFaR3ePVyHNnFqLBvWAmoARsK9QIqMJUZyKBd0SbXyPFQcB7OVd7kmVZx87VmTJMCXCKv0vkokTDWnLkK+oAuyw9hcHVal7Wp6erpDr2qnS5cuKC4udrwPAOXl5cjKynJsU1lZ6RZ1VYIGC49EFaeYJIDU9ETsGas1WQPirDqPIdBJVy1j1Jh9cxJuahmJR7d4bt8755Zk3J1li6rSDFNJFUW7hAi80T8Ze8uN2FPuew5Hr5aR6DVAnvbeEx6Trig1rOmx6pBzWC08+f4galgljtW0Quy+r8XKu9Xntp1f2RtCVvuIO+64A4cPH8bBgwdx/vx5WK1WdOzYEQMGDIBaLT+7pbS0FHPmzMHmzZuh1WrRoUMHLFy4ELfddhsA26z/vffew5dffgmNRoN+/fphwYIF6N69u+xzhRqe/KgqgwUz92pgstqKar9+oA5vZ5OX/2mXUqsMVqTFqt3kANEqOj2pM02SAFm7uUAjCZCrP2Wr/97jTQMBJWmukgBAWbs6aNAgnDt3zuW1c+fOoV27dgCA7OxspKenY+vWrejbty8AwGAwIC8vD2+++aZPn4P0aNKZeCRGKuf8yHFYqwyBu6aCXYeVJugg1z76Q48bqvEDqaQrmuBM69jQW86z8DzxvjxZY4LRyuOm1Kb8FanEV/vdRDLVVh64rHV31pW+H2T3O1OpVOjfvz/69+/v04nt5VcGDRqE1atXIzU1FUVFRcTyK4sXL0ZOTg7mz5+PcePGYf/+/WGf4OXp+t9TZnS5KDZdNuDN9uRZkVBfCrh3IrJvlxbrHqWNVMsvPeGQBPgSYaVwRuX6UCFqC8OC2X2T8JhThKZzUgTOKVSKiIbmWiXAjlJ29amnnsI999yDBQsWYPz48Th27BiWLl2KV199FYBtxWH69OlYuHAhcnJy0LlzZyxYsADx8fGYMGGCT+cmOThKR+5Jtk0soSqQV5SjDmuQsq5oJvfOZp5mAuEPdz9UbXSEx6QrurJW6bGhV5LGKhJhfWJbDQBgWvd4zB+UgsUn3KUapGMB5IStI1UmPJ+nEd1HKWQ5rFarFd999x02b96My5cvA7AtW919992YMGGCrGiAc/kVOx06dHD8v7D8CgAsWbIEOTk5WLNmDaZMmSJn6CGHp+t/H2HJ5JKeQwdKDStJJmA39MKLLcqHCKunKgGZcWqUiLRrs59XCrnL+aFqDMOBke1iMKVrHP53yYChbaLRNSUSbx1Svq+1nZQoDhqn6hXNVRIAKGtX+/bti1WrVuHNN9/E+++/j6ysLMyePRtTp051bPPss89Cr9fjpZdecqxcrVu3zucgAOnhpDUr6/Y0ECbtoZCZH/QIK43D6rRJcpT09v5YdQnHCCttlYC0kIyweq6QsfSUDi/3ScQr+2pFt7Fjv8ZJ1QBm73N3Vm37BEkSUFlZiYceeghHjx5FXFwcsrKywPM8Tpw4gXXr1uGTTz7B2rVr0apVK6rj/fzzz7jzzjsxZcoU7NixAxkZGZg8eTKefPJJcBwnWX4l3B1WMX8tr6yRKEw/pVUhg+D/kZZSSVEIe3RWWNIqWsUhUubE0F7QX8zpnNetEZXRrfD+UfFC5HKbAtDA9KreE6nisGhwCywabPt76UnpGbcvZCVEQFPdlMTQXCUBSttVABg5ciRGjhwp+j7HcZg1a5ZDK6sUpIeTPcKq1GOLZNuuikyMA9ntl+d5XNaacahSuRrbcoijyIB1to8DWkchO0GNIsIyLgB0SY5AtxT6eBat5Q1VCy2lYaVRp7WSmyUcAA5XGlHmIQEbAC6JXANC7Pc3qY67WFUipSeT1FOCmTNnIj8/H++//z4uXLiAPXv2YO/evbhw4QLmz5+PkydPOsqn0GAvv9KhQwesXbsWf/3rX/HGG29g2bJlAOCx/Ep5eeCyMP2FWNLVSkKvbQC42KAidqgSRlPNVp54Qdm7Ewnfs0kCZBbol9Cwdk+wokuyZ2OndEFhAMhWoGc1w4YvCXU0ZMa7/lbNNcKqtF0NJkRJgNnusCrz+5LmNYvytdhV6l51RemED0/8WGjAzWvK8NIe6UiVP6CJhjrf0hzHYcN94pOgH0a2khUAoK3JHbIOq8cIK50kIDUm9CKsYzdV4fcrnisSCTsXiuEp6UqMoGlYf/31V0ybNg1PPPGEy+tRUVGYOnUqzp07h1WrVlGf2F/lV+TWY/O1fhs9rvXJLBYT8dz/OUeud1dvAc4XXYawdp7eaHE5js7sfi4AuHCpGK3rrbjQ4FonkDObYKzTA/DcPMAZdXUxChp4NDa413dLjuCREc0jzVziUnvvlc6utfKM+mhI1bWT+9u04v9/e2ce30Sd///X5L6apvfFUaDlPoqASIVvlaMgICigwMqqIAqoKB64sOji4u7yE4EV5fDExd11RREEREBFdFkQQQS5ociNpVB60PtI8vsjJOSYJJNkkplJ38/Hg4c2mWZency85z3vz/sA2ug0+LXats8nWtZz+ozInQP8EAm9JVc9e2rySUxjBZz7YhZXVIvie+CrXyBX+LarQsIWx4lU94kZu8qwd1SKy2uRDNqvPBGZbgTeMHCogHXfooXB++0/XR/Yw79C4qtbCsb7ecqAW4Q1VYQpAVyo5Vg/YN8qkOvq29/qsC9eBn6sZQAOq1KpRKtWrby+36ZNGyiV3Bszh6v9SiA3EvdecWHFrY+dUqlEdnZzj82S9xXiCksIv6qRwX9r4wG4RmAbwLj8DUXVZo9eaAAQn5IOc6wCUzZdhfMCnV6rRlZ6PHCOPQfFnQS1DP06twHDMDBdLAFKXPtn9UjWgGFq0KFtNna2MOPT0zVobZRjSHPXZtrxF64BpZ5zwZ0Jqj9fSws+/rUaaTo5RrTU+I0SRPQc4IFI6c1gqoFTpS6vtYqR4450NT44wb4KEAid0xOwpvBmjqxZrkZ2douQPzcUhDgX+LarQuIrhzXcwc6Ccs8CwUAiQVIn0Agr33COsIrUsfXl71sBTpO4WsYo0DNJiZ+u8jPFMlKwdd5gI5gIKwD86aQK40KrJXXA+ZFgxIgRWLduHcxmz2SFxsZGrF27Fvfccw/nHQfSfsWOvf1K7969Oe9HrHg7/Tt4GTv3Wx2DfxV4Ogp1ZtfZvt7C+3VmK17YXe5S6ALYKv5NHBLw7eQkKh1Gh+0iz0m8qT9JK8fjnQwezirAT49VNkxqGaZ2NGBkZujNpJsybPe/v/SKxd9z4/B6rinkz2/mlr5R0US7BPBtV4WEzSnlO4c1EIQqgBIClZzx22YwnA4r188Wa+tBXykBFquVU4TVoGTwr/6BTa8UA4E6rGz94H3B562ec4R1/PjxePbZZ5Gfn49JkyahdevWYBgGp06dwgcffIC6ujqMGzcOhw8fdvm9zp07s36ekO1XxIC3C9dbP71frrMv0Vhhm+1rv+C8nXw1ZivrdC21nIFJzX0po1/qzWVitjzH9iYlp34o/vY4uHnoYxyJ4GHrHGE/T0yB9kFjwTOHtWkWXfFtV4WELU/V3nYvkgVQdppShBWwRVndAxLOcIkSBou/mgU7LURaZ+DLqVLKGE4RZJ2C4ZSaITa41g/Yi64aA7yYBXFYhwwZ4vj//fv3u7xnj/A5b2OnpIR9+oaQ7VciDZvhtFiBn67Ww2K14tZktc9t/VFrtjocDLYqWgBeKwWVMoazA5KXpsZjHW+OQGS7No0qBvC90g+A3bH+Uw8j/vbzdaTr5ZjTXVrfcbTBZqDt54mBJRwR6FJYMyq6AsC/XRUSNtNVWhfZBxGL1YrjZY1I08nBc0ctwTHIrai3Mo7C2Xk9ja7vK2Uo8xH+4tOVGpShxteXbgZAFtzGbdXlhW4x+OfJasejzRu3h75awwfenKou8Uqk6uSsba1yU1TYVWTrCpGXpg7rA0E44TqW274VW/G3LwRxWBctWsT7EqtQ7VciDZtzdr7SjIFfXAUAPNXZgHm9bGPmAj0ZHL9zYyWerQcrAJytYG8Cr5KBU4Q1M0aO9UNcq0rZonAGpSxoh/XZrjF4tis5qmKA1WG9cZ6wPai80isWd31ZzPnzU7RyKBjb6gBgS+SvM1sDnm4mdcJhV4WCzWG9dqPfTSR8nfrgYwAAIABJREFUR4vVinu2XsN/C+sQp2bwTJfosiVGhRX/GJiElcer0N6kxOOdDC7v+8tj5XM5Pr+5Bve00mJHYR2GttCieyK3ot1mBgU+H5yI/5yqQk6iChOy2YuMIw1b0dWkdno81812DrEdu3/cGY/XDlQAjM0RlyrXfUTlnbE42epAEMRhnTRpEn97bWJ4i3raeeNwJf7UwwiFjEEwqXx1Tg/V3vZ11suMY5Wc4ZTDOqyFZx4qm+Ni4NhsmmsrDUIYWFMCbpwnbP0GuTQud0YlZ2BQui5hVjVYoA5ixLOUiSa7ynYfK7kRYY3E6vwX52odaU+ldVYs5TC9R0pYAdyarHZZkXNG76cXK5/PRQyAB7L1eCBb73dbd/LS1chLD18HkmBgM1+LnXL12RzWZK0cr/URR4Q4FMrZ+mCy4GtwgC/4dFill3AhQfw5rABw/cZJE1SE1ekE8u6weouwMohlSQl4ruvNp3cGwINtPZ+E2SOs3M7OSLW7IYKDbTlVd+NhpGOcAh2dmopPbKdDtwQlWsfcdDbv8HFDuq+17eHHPd+LbcwwIR3YI6y2EykSI0s/POnaWoqt20o04z/C6vn+i7cYWbb0T5QsCjjw51RxmXQlVQJ1WDlu7sBXy7BACWg0a1lZGT777DOcOXMGZWVlLtXpgG0Jf+nSpbyJEzObz9fgveNVaGdS4JkuMVhyqBIF5Q2Y0tGA/hmuBUNcqvAGf1mMFf3iAj4ZAFsO6819sX+At2kWKjkgZ3E8+6So8a/+Kuwqsi35tGPpXsAaYVXK4H2+1U0owipu2Iqg7EvXDMPg8yGJePtoJUwqGR7raADDMNg0NAnvHqtEgkaO0a20aL/as73a3B5GTLmRBx3j9nDz0alq5DfTYMEvFYhTMfhzz1ik6KI/4hotdpVt0tU1e4Q1Avtvag6qOxo/XhdbdOqpzgb8JYgRzIxoRwAEh790iWjOVCrnGCiwF1UGWmcjSErA119/jYkTJ6KqyvYUq1Z7RlCkYlhDpajajAe+LYHFCmy7VIflR24+2f9QVI99o1OQpL15o+XinBWUN2LcN9eCqsCuc3FYAzuZvE2cqmyw4p5WWgxv6ZkKYIetS4BByXByWKspmiZq/BVBJWvleKlHrMtraTo5/nTjNXenC7BNInvGKUfZfQnz1QMVePXAzbOn3gKsvCM+YO1SIprsKtt9rMQeYY3A5X6lJsB+O1HGdT/XLJupV8kZPNvVgMUHA0ufMAbQClEK+BtSxZfTlaKV+R2VGmmuBxhhFTKHlbN3NGvWLMTHx2PTpk0oKirC5cuXPf4VFhbyp0zELD9S6TUn63qDFevOuDbT5+pEFtdacOo6+9K9L+o55LB6w+6wZhldn116p/hPomc7ebg0sAaAWW5dAJ7pYvCyJSEE7kv6neMDa17PMIxHq5tWbueYvxYwa92uo2gkmuwqm+W53mBFvdkakZSAK7XicgT4xt8R9NeRwVsUcVpHg4tT8UpPzzSBaU7dYYwqBnf7CGRIkRS11SXNyb2tYiAFa746H8QH0EIyUgSaEhBwhDVQQT7gfPR+++03PPnkk8jNzYVKxX2MZzTizzAeL3N1OrmOPguWQyUNqLrx2FMdYChDdeNsWpxrchTVPNnJgDQOS7H1LCcu14rne1pp0TvZdh51jFNgWidyWMVEyxgFHu1gu0nFq2V47bZYP7/hyZxbjI78JZ3Cs2qbS4FetPfSjCa76q09Y0mdJSIR1ig/Vfziz2H1drUlaeVYm5+IezK1mJUTgykdPW3xi7cYMb2zAaNaabFhcGJUdvP4aGACxrXRYmI7HZb1dXU6Axk9+0CWDrcls1/LgfQ8jxSBdglgu+/7QpCUgK5du6K0tNT/hk0Af1VyJ8pd+1HWhtlaP/tDGV7cW44vhiQGHWH9vzQ1Dt2finqzFQksVeBshPJ3xShl2HRXIq7VWhCnloVt8hURPK/dZsIfcmKgkQfXEHtkphYtetdAkZKJNkY5dG69srgU6F2psXgMGYgmosmuejOL12otgky6ijb8HcPsWAWu1tZ7fd9XlNBf5b5eKcMrvQJ/aJUSmTEKvPV/7ClIgURY5TIGM7oaMO4bz17J/vKMhYBzSsCNMzDQ/saCpATMmzcPH3zwAfbu3cvf3iWKvy/sSElkHVbAlnYwf/91r31YveGcwxqjlHF2VgHXdlrBoJAxSNHJyVkVMYkaeUjTWwwKW/Ntd2cV4Dae93J1dOclRpNd9eaW1pmtMEf3ar0oeCHHdy9QqTa2FwPyAE2gzEs8W4wOK1efIdgIK8csQW6fxXXD3r17Y/78+bjrrrvQpk0bZGRkQO7WM5FhGHzyySf8qRMp/kaTldVbYbFaHQYiEg4rAHx9qQ4PBFhV7W0ULBci9XcR0QmX3O5od1ijya56u481WKwwCzGbNcrwdwjz0tRY3MeEry7WoneyCn/e51r9z+fggKZGoG2tvG0uRoeV6208+KIrAdparV+/Ho899hjMZjOKiopQU+NZEBEtE1vsmC1WvHWsCkdKGvBgWx1uS7EtmXD5wqoarY62PeHOYXXmeCn38ZhAaA5rMD1jCcIOl76rl6O88jua7KpXh9UamS4BTR2GYTCpvR6T2utR3WjxcFilcRaJk7gAc0+9PVxo+Aw3RhiHwxrgxczngxJnh/Xll19GdnY2PvzwQ2RlZfGnQMS8d7wKc/aUAwA++bUax8amIkkr5zTpoarBipgbhdWRjETuKw7MYdWFcAHV0V2ICIEqDk9+hdXRvZYcTXbVm1lstFibfEFUpGFbkqYIa/BMaqfH/P3XHcGq5/2MYvW2oqCR24bwfHiymm+JYcf+FzUEuFoiSA5rUVERJk2aJHmjGgh/+LHc8f+NVmDF0UrH//vDufG6mJfOQ4mwjmrlOv2qX6q0q5yJyPJgW/9jHX8tD7zNm5SIJrvq7dGiweL9Bk6EB7acS8phDR6TWoZPByVgcDM1Hu+kx7NdfXe18eYjqGQM5vWMxcR2npMjxY59MEhDgItegjis3bt3x8WLF/nbswQ5eK0BpXUWVHCoqnNuvO7usA7MUHtM+RGKUCKsQ1to0CPRFkaOUzP4663RXUVK8MvdLbV++7vuuFwXITXCEE12lW3SFWDLYaUIa+gEcgjZbuwUYQ2NO9I1WD0oEX+71cRaROqMt/NdIWNgUsvw99y4gHtbC02wfVj5zILg7LAuWLAAa9aswYYNG/jbu8T45lIdWn1UyGnZvcKHw5qXrsb+MSnYfndSwE9aLQzygJrsj2rlu8FzKA6rSs5g89AkfD8iCT+NSkHXBIqwEtzRKhhsG57kc5viWgveOMRldpo0iSq76rXoinJYIw2bc0r+auTwljbofLsVX0dW39j/pEBLcgTpwzpt2jQAwMMPP4zY2Fikp6ezVrN+//33/KmTMC4pAW6rmlo5g0SNHIkaOX693ogPTnDPZ8lJUAbUBuqOdLXPiUHaEM8mlZxBN3JUiSBRyxlkxypQ4GPp/5Wfr2N6Z4Nkio8CIZrsqrd1J8ph5YdADmE0XitSwptT55yqEUzEWykLvEqfL+y75VLD44wgXQJUKhXS0tKQlpbG286jGefeZu4RVucpIVxHmdrJ0MsDao2RrPX9HBdKhJUg+MDfQ1ODxfZPFYXzA6LJrnpva0U5rETTwptT59weKxiHVS1jBJv+Z99toKslgkRYv/nmG/722gSw57AeKmnAR6dcI6jOhU6BNmXPSVTh9HXuhSjJfgYBkMNKCA2Xc3DO3nK0jVVgYjs9FFGUjBdNdtVXH1ahokJ8kt9Mja8uSjenmh4ZIoc3p87ZeQsqwioHIFAdamWDBXVma+CTrnjUwNlhJQKjosGComozBmy84vGec4TVV/GVgvFcWuieoMTm87WcdST6ibCG0iWAIPiAi8P67rEqAMDFSjP+HOUjIqWKt6KrKzUWvPaL9POQF/cxYc7eclQ1WFFjtmLnZe9jUImmjbfJbs4P296mYfnCNplSmEePmbvLMX9/BawB7l+QLgEAUFVVhTfffBP3338/7rzzTvz8888AgNLSUrz11ls4e/Ysf8okTmWDFW8crgRbQwGti8Pq/StIZZlalRWrwIiWGk4a5AyQoqUIKyFuAnloWnK4MoxKhCFa7Kq3wMvSI9J3VgGgmUGBVXcmYE1+InomRj5v3xhieIksfeRQe7nthhphFXqMeUmdBaV1EugSUFRUhLy8PLz88ss4efIkfvnlF1RU2AyRyWTCW2+9hXfeeYc/ZRKnssGKA9fYn8CdI6zJOu9fgVHF4MG2N7sI/LF7DGQMg/4ZGjQ32K4IX+dvslYGtZzBI+2997ukCCshNIE+NF3n0FZOKkSTXfWWEhDoDU4KCJGV8kKbwCK6zn2xs4wKpPhZbSP4Y0SmljU339nUBVMXp5LgVyhIhHXu3LkoLi7Gtm3bsG3bNlidln8YhsHw4cOxfft2/pQJyKGSBtyy5nJIn1HVYPH6ROvsJMYoZV4Lr1QyBktyTVg/OBHvd63FCzlGALYmxv8dkYyPB8bjwJgUrxrsEdqFt8Vi45BETOng6biG2iWAIEIl0IemFv8uRL/1V3CxUvpDBaLJrjalTgCRclindNCjT4oKr/Q0orsxsAe1JbfHYUhzDfqnq/H+HXHUOSCCGJQy/LN/vMfrMqcTJ5hvQy3B/H1BRrN+/fXXmDJlCnJyclBSUuLxfqtWrXDp0iX+lAnI336+jtMVoc0wr/TRrEzt5iTqlYxLVwE7KhkDhmGQl65GQZWrsYpTyzCkue8eq/Z0AIZh0C9NjcvVZrx9IxfQDhkxQmiCeWg6VNKAZUcqMb+3KQyKIkc02dUm5K9GjBd7GB1pYwUFgQVRWhsV+HhgQjhkERwY2Mwzdc/g9HAeXNGV9O7XfLa14hxhraqqQnp6utf3a2trYTaH5uSJhc0XuBc1eaOiwXtqsp+0Ugd+hmk48JbTmuaWbhBow1+CiAT6IKe+nauUvr2JJrtqbUKtqyI1CEGCK8CEE86DgbRyBvc6DfIJZlSuFFMC+ITzn9+6dWvs37/f6/vbt29Hhw4deBElFD8U1eGto/wUdey5Uue1itQ9wuoNrgnWf+7JXjXtXnAVaMNfgogEbBFWP93YAAC1ATyBVTRY8P7xSqw/WyMqxyqa7GpTMi8Rc1hpBUzS/LG7EaNaaXFrkgor74iD0cnjlGLRVTDwqZizwzphwgR89NFH+Pzzzx0Gn2EY1NfX45VXXsG3336Lhx9+mEdpkeWrC7UY+mUxZv1Yzsvn+So04Jqzx/VpqpVRgb6pnlWraW5dBprSDYWQDmzXQ+sY/9lK7gM5vGG1WnHPlmI890M5HtpeggUiarEUTXa1KdmXSA1CkJ57QjiTpJVj5R3x+Gp4Eu5q4ZrCF0ywVEU5rNyYNm0aDh8+jIkTJyIuLg4AMHXqVFy7dg319fV4+OGHMWHCBM47nj9/Pl599VWX15KTk3Hy5EkAtpvM//t//w+rVq1CWVkZevTogYULF4Yt2vDE/0ojloPlPqlqTGstlh+p8thOzzUnAOyV1iluKQGtQ+2LQhBhQMdynrcyKnC0zHdRFVeH9UR5I/YVNzh+nr+/An+4UcAoNHzbVSGJnt4N/gm0eXqwSNA/ITgSVIRVgikBfJ7CnD0YhmGwfPlyjB07FuvXr8epU6dgsVgwYMAAjB49GnfccUfAO8/OzsYXX3zh+Nl5hvaSJUuwbNkyLFu2DNnZ2ViwYAHuvfde7N27FzExMQHvyx9XayNnbt27AjzRKQarTlR7FF4557v4gy1KleqWEtA3VYXO8UocLrHdvBf1oQbshPCwPZe1MPjPCeDqsF6t8by2rVarKAoOw2FXhaIpRVgj9beSwxq9UEpA4Ph0WP/zn/8gNzcXLVu2dLyWl5eHvLw8fnauUCAlxbMtk9VqxYoVKzBjxgyMHDkSALBixQpkZ2djzZo1mDhxIi/7F4IYJeNxo8zQy7HznmRsvVCLS1VmmNQy3JasQm6qmvPnsuUBug8eYBgGW4YmYv3ZGmTo5bgjndsAAoIIJ2x9VWM4hBKOljZi8/kaj6U2d9gc4vJ6K0xqYYx/uO2qUHibdBWNUEoAESrBPDBLMSWAz7iAz7vCE088gT179vC3NzfOnj2LDh06oGvXrpg0aZJjosu5c+dQVFSE/v37O7bVarXIzc3Fjz/+GDY9kcDgpSI6M0aBKR0NmNcrFs92jQnIWQUAOcuJnKTx/HoNShkeyNaTs0qIBrZAKddAwhP/K0ONn+IrtuKsyzXCVd6H264KRShRR6ktdUau6Coy+yEiTzDnvI/BmKIlYhHWcFbT9uzZE8uXL0d2djaKi4vx2muvIT8/H7t370ZRUREAICkpyeV3kpKSUFhY6PNzCwoKAtJxc3udz+24oJZZUWfx/fWorI0Ba7Tj6/cKS1Rw/zpP/3oqqP3wQbB/oxBISSsQfXpzwAC4GSUdmdKIy1dLACj9fnZJnQWf/nwafeK8p/ScuiYH4PoA+FPBechNnr/D9dhmZ2dz2o4NMXUp4JNQ/iqVjEG9CHIKVDKwjtN2J1IOqxjSVojwEMyUSa4dhsSEIDmsfDNo0CCXn3v27ImcnBx89NFH6NWrFwDPi5VL3lkgN5KCgoKb2/8v9ObcRpXcby5snE6N7OzmAX+2i1YWzL8WA6hzeS2Um2oo+NMqJqSkFYhOvdkAXrJU4I3DFWhtVODP/VLw3rEq4CK3FnOV+hRkZxu8vn9AVg0cK3V5TRGXhuws14dUqR1bsRGKHx5AfWlYaW1U4LifYj8AMIvAuSakjS4I51OSKQE8fpZfMxGpJzyDwYD27dvj9OnTjrzWK1euuGxTXFzsEXUVE96W+52JCbJJuj+uNzSlGl0i2niuWwzOPZCO7XcnIzNGEVC0zV5E6I0alnDY5Wphm/FHY+QsFAukEMGN+KG2Os6thiIVYSWil2AirEqOQ4fEBJ+mzm+E9YknnsD06dM5fRjDMPjtt9+CElJbW4uCggL069cPLVu2REpKCrZv345bbrnF8f4PP/yAefPmBfX54UbOAHqlDIDvG2GfAHNTudI7WYX9Tq17WnKosiYIsdLe5D8dwM7xMt8OazVLDmuRgDmsQOTsaiQJpehKDCudf+xuxKivijltSw4rESpsrSj90dQjrH4d1h49eiAzM5PHXdp48cUXMWTIEDRr1syRw1pdXY3x48eDYRhMmzYNixYtQnZ2NrKysrBw4ULo9XqMGTOGdy18oFcwfqOnegWDaR29L12GwtSOBqw8XuXIv3rjdmnPWCeaNmPbaDFvXznK6v17BiV1vmN7bA5rRYOwHke47KqQhLJKLvR9+KVbjEjRyTlPlopUlwAietEGkQdDba38MHHiRNx333087tLGb7/9hsmTJ+PatWtITExEz5498fXXX6NFixYAgKeffho1NTWYOXOmY3DA2rVrw9KDlQ/0SgYGL09MzQ1y3Ndaiwey9IhThydZKzNGgW+GJ2HLhVr0TlYhj7oAEBJGr5ThuxHJ+PTXavx1v+/JVGVBOKxVNxzWJYcqsPpUNZb2jUMkLUu47KqQhOSwCtzAyR4d9uY435vp2jrNTBlYRIgEkxIgxQirIJOu+GblypU+32cYBrNnz8bs2bMjpCg0dAoGBi89J7YOTUK6PvxL9F0TVOia4DmilSCkSGaMAjNzjFhxtMpnFLW83gqL1eo1OsbW9qqywYJvLtZi7k/XAQB1FmtEHdZoJBQfTuiUXrt2tgBWbooKL/VwnYxGKQFEqARXdBUGIWEmokVXBDfmdDcigaXvKRBcrgpBEDb+dqvviWxWANd9pA6wOqyNVnx5vtbxs8UKXK6j6zQUpJwSYNfOpuPLoUkeY62b0pAEIjz4irDKGHZHT4opAXxCDiu490VM1MjQ7EakNEMnR7rOdviGNNfg7kwtusSzF4qQw0oQwXN3Sw3uTPddrFjmo3lmFcvg94oGKw5cq3f8POm7Epyqous0FELpLyv0fdjhsHKMB1GElQgVXw5rtlHB2nVILfSTXRBELCWgtLTU19tRg59BOQCAt/rF4f42WjAAas220DzD2OaZa+W2cavdEjwdVjkjzekUBCEW9EoZ1uYnoMZsRfo/2QeHlNVZ4G1Nny3CWlZnwdXam50CimosaG+ITGJitNrVUI6e0Pdhu68t52iryWElQsVXICtVJ0chS+s9sfQrDoSoGBwgJuo4WJ9UncyRI6d1OmrOJ13HOE+H1WyNzp6LBBFJGIbxaeDLfURY2RzWi1WuN4M0nQyJlP4dEqGsknOtzg8XlhtzuriqIItOhIqvCGuKTsaaYiP0SkQwMAx/T3cS9Nf5h4vDqufwaKOSM0jT0SEliEjjq/1VNYfrO11HfYtDJZQcVqFvxL5yWNl48Raj/40IwgdaHyd9qlbOumIh9INdoChlwMBE/npek3cFoI7D8eQyxQoA2sZyb3hOEAQ/+GptxdbWyh3KMw+d0NpaCYtdu5yjQ9A9UYmJ7XT+NyQIL/iOsMpZe/0KnTrDlV5JSqTrZFjUxwQjj+v4QtsJUcBlDCRnh9VEWRYEEWl8FV2xpQS4E60O66JFi2AymTBz5kzHa1arFfPnz0f79u2RmpqKYcOG4dixYyHvy76sHgxCB44CjbAyDIN5vXx3ryAIX/iyOWlaGWuvXylYqQS1DF8PT8bRsWl4sK2e188mhxW2wil/xHCsnHogy/Wpe2BGeEaxEkRT5IFs9qhWRYBtrdwJZuqM2Nm7dy9WrVqFTp06uby+ZMkSLFu2DK+++iq+/fZbJCUl4d5770VFhe8BDf4ILSVAHDmsgUSwFH40D28R+PCWeKfBMrkplFQdzXhLCdArGAxspmGNwEohwhpOjdFnpQPkUpUZ5X4m5QC2SVZcyElUOW6qBgWD6Z3DM4qVIJoiz3QxOFrLOeProZNLjnowU2fETHl5OR599FG8+eabMJlujmm2Wq1YsWIFZsyYgZEjR6Jjx45YsWIFKisrsWbNmpD2GVrRVUi79gmXZus3UwK4fy7bM06fFBXi1TIMbqbGQ+0Cjy79PdeEdJ0MbWMVeIUiuFENm80xqRgs7GOCUSVDitbTzvF9nRhVnh/I9logkMMaJhb+qkSnTy5j8JfFfrdVBvAtLOsbhwNjUnD4/lQakUoQPJIVq8Tue5MxraOrM+DTYeUQ+tNHmcNqd0jz8vJcXj937hyKiorQv39/x2tarRa5ubn48ccfQ9qnWNtaeZtA6Iz9FAmkowubc7sk14TTv0vD6kGJLtFSrozM1OLo2DTsGZWCHkkUYY1m2BzWgvFpGH9jlTaFpYCb4TkpwMTyNNcsxALUcF7LTTbhcn9xPVYXhq9AKjOmyR5agggrBqUMHdxayNX4cFjrORRVRlOEddWqVTh9+jTefvttj/eKiooAAElJSS6vJyUlobCQvcctABQUFPjdb0WlCsHeUurragF4v1Gmqi24XBdcfEWDRviLzZSWlaGg4Cqqqzz/Bt9/u2uKyqUL58BctZ2LFytkAAILWPg7zly+B7EgJa2AMHqbazS4UGs7N9PVFpw5dcqRz20we56LRZcLAfCXZljX0AD3a0NpqYOva9EfFnOjx7Hkemyzs7N9vt8kvaplRyoxZ0855+1HZlKUlCDEhMYtvOVr2b8ppQQUFBRg3rx52Lx5M1Qq7xE690ii1Wr1GV30dyMBAN3ZYqCkjrtY59/VaIGKeq/vj8024tT1Rvy3sA6d4pTYfcX7tu6YdCr8VtfocxtjrAnZ2SbEnL8GlNS6vOfzb//fJZcf27TKRAuD7bZafa0e+OWqx6/kJCihVzLYednzb/C1r4KCAk7fgxiQklZAOL1LdLV4amcZzFYrXs9NQNvmN32N7NJybL1a6bJ9enoacLyEt/3XWuWAW7GkRqMFrnO/vtxRKZXIzm7u+JnPY9skHdZMg/+nh3/3j8dnZ2oQo2So5x5BiAy1m8Na02jFwWv1WHm8CnUWYExrLQZkaGC2WDlNstMJ3QiUJ/bs2YNr166hT58+jtfMZjN27dqFlStXYvfu3QCAK1euoFmzZo5tiouLPaKugRJSWys/h9+gZPDvAQkAgAuVjejyaRHnz+ZSMGu5kYAb6mngXIjF1jNz/+gUtLrR52fs18XYejE4B5+IDvpnaHD4/lTW91JZUgL4zuG0FTu6Xrih5KID4c0zbZI5rN39jLQZmanBsJZarLwjHktuj0MSS/IzQRDC4V5hW1JnwfAtxfjHyWr851Q17vv6Gg6VNHDKXwWiJ8I6bNgw7Nq1Czt27HD86969O0aPHo0dO3YgKysLKSkp2L59u+N3amtr8cMPP6B3794h7TuUHFZ/qaOMH0fQF1xaEgba1spO5/ibqSlxagYp2pu3VLaba7zm5qvPdXOdJTyjCxXoEje5r7UOzmZpeAsN7+3fprjVAjzfNQbWENrTAVR0xTtpOhnSfUykUkuhdwRBNGHcI6x7rtTjulNrK4sV+OTXak75q0D09GE1mUzo2LGjyz+dToe4uDh07NgRDMNg2rRpeP3117FhwwYcPXoUjz/+OPR6PcaMGRPSvsM56crZJAdqngNyWAMsavlrr1ik62SIV8vw9z5xkDuJa2A5IEYnLb2SVHisgx5aOYPeySpM6UgOK3GTFJ0c83rFQq9gkGVUYFZ3I+/OYL9UNR5uq4NGbutw8VhHfYjuanhb1DXJlACGYTC7uxHTd5axvs+lqpQgCOHQulkutjTVA8X1nHosAzccVv4mCIqap59+GjU1NZg5cybKysrQo0cPrF27FjExMf5/2QehpQT4vsk5vxuodebUJeDGf+UBfnheuhpHx6axvne9wfOAOEeKGYbBgttMWHCbyWM7ggCAxzsZ8Hinmw8yv1Xxa6RUcgav3x6H12+Pc7wWakqAJdQP8EGTdFgB4Pdt9Th56QrePOuZHsB1qhVBEMLgHmFl45eSBs4OqzbCb/ERAAAgAElEQVSKHdZNmza5/MwwDGbPno3Zs2fzuh/3Ix2vlqGEQ49rwH/U1MVhDTTCyiF6fjPCyh/dE5VQy2+O/u6dTG2qiNDgErzMMipw6rrvIkM7bJdGqO4my3MabzTpUGK2nt2YksNKEOLG25QYZ67XW1FQzs1wR0tKgJC4R1ZMATQg9/d1MiGkBARSdMXnamaMUoa/9IqFVs6gmV6Ov95KgwCI0PB37rc3KTxyo33B1l8+VH+zMZSlFj802QgrABi9/PV6SgkgCFHDJcIKABcquYVNyWENHff7lEktAyq4HX9/Ftc1wsp/0ZXd1+Y7/+7RDgY82sHgt20YQXDB33Wy+94UHCjm3pJKxVJPHuqKfkMo1Zd+aNKeWYyC/ZuJoQgrQYgarlX9RTXcHCYt25xNIiDcrSnbFB1vPNnZd1QopJQAFns+IMO1+fpjNwqewlVvS84qwQdczk9FACdxoBHWZK3/a7o+jBHWJm2ljV4cVi45TwRBCAfXCOsVjg5rtPRhFRLWCCtH+qSo8FBb29Qo51ZRDkLqEuCpY2a3GHSKsy2xTWqnR49EZVCfTRCRxf8JGsgCMWsOqw9/M5VDi89GirCGB4OXlADqEkAQ4oZLDisAXK7mZj1NavJUQsUzh5W7HVXIgCW3x6FsYgb+NzLZ4/3QugR4freZMQrsvCcFe/tWY3GuyREBpecWQsxweaBii5oGsq2v+Giaj3agduo5FroGQ5P2zBQM+/I/FV0RhLjh+kzJJcKaqJEhQUPDQULFM8LK3Y76bWsVwuAAtvxkb7fUh9q6NlK/v7U2oH0RRDhh6+3rTiDZTWx29IUc7+k5T3fxX9DFZbJgsDRphxVgX7bSk8NKEKKGa05gUY3/COveUSmhyiHgOekqkAirP0LJYWUrpDJ7ufF3S1Bicnub05plVPi8eRNEpCnj0CYukAgrW77r4GYajGipYd3eOXUny1vVehgRjcO6aNEimEwmzJw50/Ga1WrF/Pnz0b59e6SmpmLYsGE4duwYr/tlM6pc2qAQBCF+/BVddTQpEBdAriXhHffct1ixOKwsMrxFgRiGwcI+Jlx9KB17RiUjK5Yln5YgBKK0novDyv3z2JxblZzBqjvjMc1tbCtguz6W3B6Hy79Px95Rnqk74UYUlnrv3r1YtWoVOnXq5PL6kiVLsGzZMrz66qv49ttvkZSUhHvvvRcVFRW87ZttRCulBBBEdOCvxUq3RGrmzhfuQUteHVbnoqsAf1cGIM4tPSFN5zsFRCljAk49IIhwM7gZe+QTAB7rYHMwFQGct96cW4ZhMCHb1WEd0vzmvjUKBgzD4G4vkdhwIbjDWl5ejkcffRRvvvkmTKabI+qsVitWrFiBGTNmYOTIkejYsSNWrFiByspKrFmzhrf9d0mgSVcE0VTJSaAIGl94pATwWMgWWkoAsLxvHIxKBkoZ8Pc+Js5dJghCTDQzKPD7jAYAQIpWhuYG24NXpziFI780kAirr8ugU7wSj9xIj2lpkGNuD6PHNs8HMKSADwQfHGB3SPPy8rBgwQLH6+fOnUNRURH69+/veE2r1SI3Nxc//vgjJk6cyMv+u7C0UOFagUwQhLTpnkgOK1+E0iXAH84WOdCepjKGwV0ttDg1XgMruLdEIwgx8lSrBizo3xJyxuac1pitUMsYyG88yQWSw+rvWlrUx4S/9IqFUsae79otQRXQCOZQETTCumrVKpw+fRpz5szxeK+oqAgAkJSU5PJ6UlISrly5wpuGbiwRFmryTBBNA9aen0RQuOewquRMQNEeZ3oluX4v+c25Lz0207su97e/0W9VJWfIWSWiAq2CgUpuW5bXKWQOZxUILMLKdV++hhFEckqgYBHWgoICzJs3D5s3b4ZK5T2PzN159DfirqCgICAdDZfPoE+cGj+U2ozc6NSGgD8jUohVFxukNXyQXhvPtVZg0engc1CHJTfi0plfXV7jqjU7Ozvo/UYr7jmsMgZ4q18cnvxfGRqt1oBGNs7vbcLvtl3DlRoLpnc2IDPG9Vb17v/F4dH/lrL+7uI+Jkz+vgRVjVa83NNIRbREk4JhGKzoF4cZu0pRx21uSkhE8vISzGHds2cPrl27hj59+jheM5vN2LVrF1auXIndu3cDAK5cuYJmzZo5tikuLvaIujoTyI2koKAA2dnZWNvKio9PVUOrYHBfa63L04pYsGuVAqQ1fJDem7yUDfRoVYPfbSvh/Dsv5MSgmV4OOQPc11oHlVPETWrHVmy4+6MyAKNb6xzFGv3WX8HpCm530J5JKhy6LxX1Fiurw3lfGx2e+6EM1xs8y/3zm2twYlwazFYrDYEhmiTjs3QY0VKDT36twTM/lIV1X4GkIISKYFfzsGHDsGvXLuzYscPxr3v37hg9ejR27NiBrKwspKSkYPv27Y7fqa2txQ8//IDevXvzqkWrYDCxvR7jsnSidFYJgmBnaAst/t7H5H/DG8QoGDzYVo8HsvUuzioROu45rPaFML1SBr1ShhldAyvQUMsZn9FRNlNtT/HSKhhyVokmjV4pgzYCy/WBDCoIeV+R25UrJpPJpSsAAOh0OsTFxaFjx44AgGnTpmHRokXIzs5GVlYWFi5cCL1ejzFjxgghmSAIEaILoKuHJoL5Vk0N9xxWmdvc8zGttfj012rsuFzPy/7YUgz+3NOzkpkgiPARyQir4F0CfPH000+jpqYGM2fORFlZGXr06IG1a9ciJoamjxAEYSOQrh4UdQsfHikBbl+LTiHD+iGJeGZXGVadrA55f/Us06ruSI9sX0iCEDORqB/nsRmIX0TlsG7atMnlZ4ZhMHv2bMyePVsgRQRBiB22KtX2JgWOlzV6vE49lsOHu//IdrOUMQxvk8UCKeIiiKZIJKydrw4CfEPhBoIgJA1bnpY3pyiGHNawwdYlgA331AG+oOA5QUSeJlF0RRAEwQd6Foc13ovDSikB4cMzh9XLdmHav4oKZgnChUhcEZE0qWS9CYKQNGwRVu8OKzk14cLi5orKvCTQsaSe8oJK7n8bgmhKRCKH9cG2epefR7XShm1f5LASBCFpAnFY2aKxBD9wTgkI0/4pwkoQkWd4Cw3uTFcDADJ0cszKCV9RvKiKrgiCIAKFregqQeMth5We0cOFR9GV1+3C47JGMpeOIKRAJK4IuYzBZ/kJuFhlRoLa1nM5XJD1JghC0rA5rN6KriglIHy4u6He/Ee+UgIebe+6FPl4JwM/H0wQUULPpOBHVweCjGHQwqAIq7MKkMNKEITE0bD0YTWxOKwaeWRbsDQ1Ip0SML2LAZkxtsTV7olKPNhWx9MnE0R00DJGgUc76P1vKBEoJYAgCEnDVtwTy9LNmjoEhBf3pX5vRVd8OawtDArsuicZRdUWNDPIKSWAIFh47TYTpnc2YNqOUuzkacqcUJDDShCE5BnSXIMtF2oBADkJSrQweJaMU8FVeFk9MAENVuDChYtIz8jwmn7BZwqrTiFDKyM9iBCEL1oYFJjSwYCdl0scrw3IUAuoKDjIYSUIQvIs72vCa79UoM4MPN8tBmqWFkeUvxpeeqfYboAFFRZk+xiRGq62VgRBeGdYCw1uT1Vh5+V6JGlkeLlnrNCSAoYcVoIgJE+8Ro75vU2On+vMnl4RdQgQB+HqEkAQhHfkMgYbBifi1PVGpGjlrHn+Ykd6igmCIPygljMwukVUU3XUWV4MDG3h2li8W4JSICUE0bSQyxi0Mykl6awC5LASBBGlPNv1ZgNrjRxRVS0rZQY2U+P/0mzpAyYVg0V9TH5+gyAIglICCIKIUmZ0jcHITC3OVTaiS7wSCRqKsIoBGcNgXX4Cjpc1IlUno++FIAhOkMNKEETU0sqoQCsjmTmxIZcx6BRPqQAEQXCHUgIIgiAIgiAIUUMOK0EQBEEQBCFqyGElCIIgCIIgRA05rARBEARBEISoIYeVIAiCIAiCEDVMWVkZjR0hCIIgCIIgRAtFWAmCIAiCIAhRQw4rQRAEQRAEIWrIYSUIgiAIgiBEDTmsBEEQBEEQhKghh5UgCIIgCIIQNeSwEgRBEARBEKKGHFYi6igtLRVaAkEQRFRBdpUQmqh0WK1W8beWbWxsRF1dndAyOHH06FF89913QsvgREFBAUaMGIENGzYILYUzUjhf3ZGSZilpFTNSOI5kV8MD2dXwIyW9QmmNGoe1sLAQP/30EwCAYRhRf/knT57EjBkzMHLkSDz//PM4dOiQ0JK8cujQIfTt2xd79uwRWopfDh48iLy8PBw+fBh79+4VWo5fTpw4geeeew73338/XnnlFWzdulVoST65cOECNm7cCIvFIvprTEpaxQzZ1fBAdjV8SMmuSslOiUGrfNasWS9HfK88c/LkSfTt2xcHDhxAhw4dkJGR4TigDMMILc+FI0eOYPjw4cjMzETnzp3x6aeforq6GoMHDxZamgcHDx7EkCFDMHXqVLz44otCy/HJoUOHMHjwYMycORPjxo3DwoUL0b9/f6SmpgotjZUTJ04gPz8fmZmZyMrKws6dO/Hll1+iuLgY/fr1E1qeBwUFBcjLy8P+/fuRmJiIdu3aQSaTifIak5JWMUN2NTyQXQ0fUrKrUrJTYtEqeYe1uLgY06ZNQ9u2bXHx4kXs2bMH7dq1E6VxPX/+PO6//37HhZ+XlweDwYBTp07hzjvvhFqtFlqigzNnzqBfv3546qmnMHfuXNTX12PNmjXYtGkTTp48CZVKheTkZKFlArh5A3j88ccxa9YsmM1mbN68Genp6ejZsyfMZjNkMvEsJtTU1OAPf/gD+vbti8WLFyMvLw/5+fn48MMPsWnTJpSUlGDQoEFCy3RQUlKC6dOnIzs7G0qlErt374Zer0f79u1FZ2ClpFXMkF0ND2RXw4eU7KqU7JSYtIrnbAuSc+fOoVmzZnjhhRewefNmFBUV4U9/+pNj6UIsX7rFYsH333+P/Px8PPnkk47Xjx8/jqNHj+LOO+/Eww8/jHfeeUdAlTYsFgs2btwIjUYDk8kEABg/fjyWLl2KjRs3Yt68eXjhhRfw6aefCqzUZqQeeeQRPPbYY3jppZcAADk5OejXrx+WL1+OmpoayOVygVW6olKpUFRU5IhS1NXVITk5Gf3798ewYcOwc+dOrFq1SmCVNyktLUWLFi0wdepU/OMf/4DBYMDbb7+NdevWiW4pS0paxQzZVf4huxpepGRXpWSnxKRV8hFWvV6PNm3aoFu3bjAYDBg5ciTeeecd7N27F23btkVGRgYACP40yDAMUlNTkZmZiVatWgEAFixYgGXLluGJJ57AwIEDce7cOfz3v/9FTk6OoEsuDMOgefPm0Ov1+Oc//4lXX30VLVu2xLJly/Dss8/innvuwbZt23DgwAEMGzZM0AiGUqnE0KFDMWLECAC2oguZTIbU1FSsX78eer0e3bt3F80Tq8ViwfXr1/HPf/4TJpMJ/fv3h1KpxMWLF/HKK6/gwQcfRFFREc6ePYtRo0YJLRcAYDAY0KFDB3Tr1g06nQ4DBgzAli1b8MMPP0Cn06FDhw5gGMZx7IVEr9dLRquYIbsaHq1kV8OD1Owq2dTgYMrKysThxoeAxWKBTCZDfX294ynrrrvuQkpKCubNm4euXbvi7bffRmpqKu6//35RaK2rq8Mbb7yBnJwcxzLF2bNn0atXLyxduhRjx44VVCcAXLp0CR988AGOHj2K2bNno0uXLo739u3bh4EDB2LLli3o3bu3gCptmM1mlyf+6upqjB49Gnq9HmvWrBFQGTvr1q3DpEmTkJ+fj4yMDKxevRpjxozBkiVLsGPHDkyYMAG7du1Cenq6YDcEtptRQ0MDlEolSkpK8Mgjj6CqqgpTpkzB0KFDsXjxYhiNRkyfPj2iOu3XlF2v/Vyw2wMxaZUSZFfDA9nV8CF2u0o2NTQk57CePn0aGzduRHFxMbp06YL8/HyYTCbHAW5sbIRCoXAY19TUVBiNRmzfvh07duxA27ZtRaPV+aQAbFV4kydPxssvv4zc3NyI6WTTOnDgQMTHx6OwsBBnz55Fjx49oFKpHFp//PFHPPXUU/jkk0+QmZkpqFZvx/X777/H2LFj8e677+Luu++OqEZvejt37oyBAwciISEBX375Jf71r39BrVbj1ltvxbRp0wAAa9asweLFi/Htt99Co9FEXO/Vq1dhsViQkpLCamDtxstutGpra8EwDH7++Wds27YNnTp1ipjWU6dOYdWqVSgtLUXz5s0xceJElxxAuz0Qg1YxQ3Y1MlrJroZHr9jtKtlUfpCUw3rs2DEMGTIEffr0wZkzZ6BQKFBTU4PVq1cjOzvb42ngwoUL6Nq1K0wmE9avX4+uXbuKRivg+bT1l7/8BVu2bMFnn32GlJQUQbVWV1fj448/Rrt27RyGypl58+Zh586d+PjjjxEXFyeoVrZzALDdqB566CHccsstWLhwYcQ0+tNbXV2N//znP2jfvj0aGhqgUChczoPZs2fj5MmT+PDDD6HX6yOq98SJExg3bhx69eqFuXPnIiMjw6eBvXz5Mnr37g2GYbBx40aXaFG4OX78OPLz8zFw4EBcvXoVFRUVuHDhAt5++20MGDDAoVkMWsUM2dXIaSW7Gj69YrWrZFP5QzIOa319PSZMmICkpCQsW7YMFosF+/btw6uvvoqff/4Za9euRU5OjuNA1tTUYO7cufjoo4/wzTffoH379qLTaj9pDx8+jHXr1uHdd9/Fpk2bInqC+tK6b98+rFu3zkXrwYMHsXbtWqxcuRJffvklOnfuLAqtzsfV/gQI2G4A//73v/Hzzz9H3Pnzpfenn37CunXr0L17d8c5e+zYMaxcuRKrV6+O+LEFbEuVDz74IK5fvw6TyYQuXbrg2WefRbNmzVgNbG1tLf74xz/ik08+wddff40OHTpETKvZbMZjjz0GAHj//fdhtVpx+fJlvPLKK/j888+xYsUKjBw50qFbSK1ihuxq5LWSXQ2fXrHZVbKp/CKZqoPGxkZcvXoV3bp1AwDIZDL07NkTS5cuxW233YYxY8agsLAQcrkcVqsVhYWF2LdvH9avXx9RoxqIVoZhcOnSJSxcuBDffPNNxI2qP619+vRx0Xrx4kXMnz8fX331FTZt2hRxh4rrcVUoFGhsbAQATJkyBdu2bYu4UfWnNzc3F/fdd5/jnL1+/TqOHDmCCxcuCHJsAWDPnj2IiYnBe++9hxEjRmD//v1YvHgxLl68CIZhYLFYXLavra3FyZMn8fnnn0fcAWQYBlevXnUU2jAMg7S0NCxfvhxjx47FE088gV9++cWhW0itYobsauS1kl0Nn16x2VWyqTxrlEqEFQBGjx4NhUKB1atXu7x+9uxZTJ06FYmJiVi5ciVUKhXMZjNqa2sFuaC4an3//fehVqtx9uxZaDQawSpYA9F6+vRpaDQapKeni1ar/RwQA4Ec28rKSlitVsTExAiitby8HD/99BMGDBgAAHjjjTcc0YpnnnkGzZs394gK2AsGhODRRx/FiRMn8N1330Emk7kUBjz00EP47bffsGXLFmi1WsG1ihmyq8JrJbsaGFKxq2RT+UVSba0qKyuxY8cOqFQqdOrUyZH/YzKZUFVVhW3btmHMmDHQ6XSQyWSCXlxctNqrLU0mEwwGgyS0xsXFCeZQcdVqPwfEQCB6VSqVoK1sNBoNWrdu7fi5d+/eqKiowPbt23H27Fl06tQJsbGxWLFiBbKysqDVagXpxWg38FqtFt999x2KiorQu3dvKJVKmM1mKJVK6PV6bNiwAXfddRcSEhIAQHR9I8UC2VXhtZJdDQyp2FWyqfwimZQAABg7diwyMjLw/vvvY/369airq3O816VLF9TW1qKyslJAhTfhorWqqkpAhTeJNq1iOQcA6em1Y1+qmj59OkaNGuVYypoyZQrmzJmDkpISwbTZoxH9+vVDbm4uvvrqK49m5i1btgQAl+NNsCOlczTabJWUtIrlHACkpxcgm8oHknFY6+vrHRMW4uPjsXTpUrzxxhuoq6tDRUUFtmzZAqPR6JggQlpJq9BITa9zPpW9lRFgM7D33nsv/vWvf2Hz5s347rvv0KZNG6FkArAdW41Gg7lz56JHjx74/PPPMXPmTJSVlaGwsBCfffYZ1Gq1aGeeiwUpnaOklbQC0tJLNpVfRJfD6lyJaMeeR3Hx4kVcvHgRXbp0wZw5c/Djjz/izJkz6NSpE86ePYu1a9c6ErFJK2klvYHrLSoqwr59+zB06FAANxtIv/DCC/jkk0+wefPmiBYDlJWVQa1WO3KmnLWeP38e+/fvx1133YXly5fj008/xbFjx9C+fXuUlJTg448/Rk5OTsS0ihkpnaOklbRKTS/Z1MggqhzWgoICLFiwAB07doTRaATgeiAHDx6M+Ph43HHHHbjzzjsxePBgtG3bFgMHDsTMmTPRrl070kpaI6Y1GvUOGjQImZmZuO222wDYloo2b96MOXPmYMuWLRGttj527BgGDhyI1q1bOyrSLRaLQ+uQIUNgMpkwaNAg9OrVC+PHj8ctt9yCe+65B9OnT49oM3sxI6VzlLSSVqnpJZsaORT+N4kMR44cwfDhwzFq1CiUlpY6+pTJ5XJcunQJvXr1woQJE/D888/DarVCrVYjMzMz4tNASCtpjXa9Tz/9tMvv3XXXXTh27FhEl4IOHjyIu+++GzU1NVi1ahXy8vJgMpkgk8lw5coVDBw4EMOHD8e8efMA2JL/9Xo9hg0bFjGNUkBK5yhpJa1S00s2NbKIIsJ67do1jBs3DsOGDcNrr73mmEZSXl4OjUaD2tpaKBQK/PnPf4ZcLhdstjppJa1NRa8ds9kMmUwGvV4fsb/h0KFDGDx4MKZOnYrf/e53+OijjzB69GjEx8cDsM0z1+v1eOmllwQ/rmJGSucoaSWtUtNLNjXyiCKH9cCBA3j++efxxRdfQKVSYerUqbh48SKOHz+OCRMmYPLkyWjRooXQMgGQ1nAhJa0A6Q0Xv/zyC/r3748ZM2bgpZdeAgD06tUL3bp1w3vvvSewOmkhle8cIK3hQkpaAWnplYrWaLKpokgJuH79Oqqrq2E2mzFq1CgolUqMHTsWlZWVWLp0KYqKijB37lzBmiqTVtLqDunln9raWrz33nuYPn06XnrpJUeF7ZgxY7B+/XoUFBR4zDUnvCOF75y0klZnpKRXClqjzaaKwmGNjY3F2bNnsWnTJqSmpmLOnDlo3rw5AKBz584YP348Bg0ahDFjxgislLSGCylpBUhvONBoNJgzZ44jr4thGDAMgzFjxuDNN9/Epk2bMGPGDEkYVjEghe/cDmkND1LSCkhLrxS0RptNFSyH1Z7HAQCpqak4e/YsXn/9dVy8eBEPPfQQYmNjYbFY0KpVK+zcuRNVVVXIz88XQippJa2kN8w0NDRALpe7TCayz6xOSEhAeXk5Pv/8cwwaNEgU/RXFipS+c9JKWqWmV0pao9GmRnxwQHFxMQBbBZrZbHa8/uCDD6Jv374oKyvDkSNHbOJunBgKhUKQXBDSSloB0hsJrfYRgO7Y9fXp0we//fYbCgoKALg25Cak+Z2T1qarFZCWXilqjUabGtEI64kTJ9CtWzecPHkSI0eOhEwmczwFZGRkIDk5GefPn8ebb74Jo9GI06dPY82aNdi6dSvmzZvnqGgjraSV9EpfL5tW5wiGM1lZWdi7dy82btyI3/3ud1AqlRHTKXak/p2T1qalVWp6pa41mmxqxBzWwsJCPProo0hLS8NPP/2EY8eO4e6774ZcLkd9fT3kcjkyMzORm5sLrVaLf/zjHzhw4ACuXLmC9957L6KTIEgraSW9wmhlM7AWiwUMw6CoqAgHDx7EmDFjoNPpIqZVzETDd05am45WqemNBq3RZFMj0tbKarU6ZuY++eSTuHLlCp566ikMGTIE77zzDgDbHFuVSuX4ncLCQhiNRpjNZsf0iEhAWkkr6RVeK9uow5qaGpSUlCAjIyNiWsVMtH3npDW6tUpNb7RpjQabGpEIK8MwaNasGYxGIwYNGoTs7Gy0adMGb775Jo4cOYIRI0ZALpejsbHRUcUWExMDlUoFtVodbnmklbSSXpFpdY8KWK1WKJXKiN9gxUy0feekNbq1Sk1vtGmNBpsasZQAnU6Hdu3agWEYyGQytGrVCm3btsXSpUtdDugnn3yC2NhYxMbGRkIWaSWtpFfEWlevXi24VjETjd85aY1erVLTG41apWxTw5YScOHCBRw9ehRFRUXIz8+H0WiETqeDxWJxePi1tbXYunUrpk+fjiFDhiApKQnLly/HwYMHHf3MIgFpJa2kl7RKASkdR9JKWqWml7SKm7A4rIcPH8aoUaOQkpKCc+fOwWAwYPTo0Zg8eTJatmzpSPhlGAZ1dXXYtGkTHnnkEZhMJqxbtw45OTl8SyKtpJX00rkgaaR0HEkraZWaXtIqfnjvw1pWVoYnn3wS48aNw4YNG3D+/HlMmDABe/bswaxZs3DmzBmXajW1Wo3vvvsOer0emzdvjuiBJK2klfSSVikgpeNIWkmr1PSSVmnAu8NaUVGBa9euIS8vD3FxcQCAP/7xj/j973+P0tJS/O1vf0NRUZFjFNjGjRuxfft2fPHFF2jfvj3fckgraSW9dC5IHikdR9JKWqWml7RKA94dVrlcDq1Wi0uXLgGwtVIAgAkTJuC+++7D4cOH8e233zq279u3L7Zu3SqI109aSSvpJa1SQErHkbSSVqnpJa3SICw5rOPHj8f58+exYcMGJCQkwGw2Qy6XAwB+//vfo6ioCF999RWsVqvjKUAoSCtpBUgvaRU/UjqOpJW0AtLSS1rFT8gR1srKSpSVlaG0tNTx2tKlS1FVVYVJkyahurracSABYMCAAbBYLKivr4/4gSStpJX0klYpIKXjSFpJq9T0klZpEpLDevz4cTzwwAMYPnw4evbsiffffx/V1dVISEjAu+++i19//RWjR4/GsWPHUFNTA1kKxaIAAAL2SURBVADYv38/YmJiYLWGfcAWaSWtpJe0Sg4pHUfSSlqlppe0SpegUwJOnDiBoUOHYty4cejduzcOHjyIv//97/jiiy/Qp08fAMDRo0cxefJkVFVVITY2Fmlpadi1axc2b96Mzp078/qHkFbSSnrpXJA6UjqOpJW0Sk0vaZU2QTmspaWleOSRR5CVlYUFCxY4Xh89ejRSU1OxbNkyl9yJ9957D5cuXYJGo8GoUaOQnZ3N319AWkkr6aVzIQqQ0nEkraRVanpJq/RRBPNLDQ0NKCsrw8iRIwHAkfDbunVrFBUVAbDNtrW/PnnyZP4Uk1bSGgSkl7SKHSkdR9JKWgFp6SWt0ieoHNbk5GS88847uP322wEAFosFAJCamuqS/CuXy1FcXOz4WYicCtIaHqSkFSC94URKWsWMlI4jaQ0PUtIKSEsvaZU+QRddZWVlAbAdSKVSCQCor693OXgLFy7EwoULUVdXBwCCVayR1vAgJa0A6Q0nUtIqZqR0HElreJCSVkBaekmrtAkqJcAZ5xFgABze/1//+lcsXLgQ33//PdRqdai74QXSGh6kpBUgveFESlrFjJSOI2kND1LSCkhLL2mVJvJZs2a9HOqHWCwWMAyD3bt3w2q14tSpU1i4cCG++eYbdOvWjQeZ/EFaw4OUtAKkN5xISauYkdJxJK3hQUpaAWnpJa3SI+QIK3DzCUAmk+Hf//43jEYjtmzZIspRYKQ1PEhJK0B6w4mUtIoZKR1H0hoepKQVkJZe0io9Qp505Uz//v0BAFu3bkX37t35/GjeIa3hQUpaAdIbTqSkVcxI6TiS1vAgJa2AtPSSVukQ9OAAb1RVVUGv1/P5kWGDtIYHKWkFSG84kZJWMSOl40haw4OUtALS0ktapQHvDitBEARBEARB8AmvKQEEQRAEQRAEwTfksBIEQRAEQRCihhxWgiAIgiAIQtSQw0oQBEEQBEGIGnJYCYIgCIIgCFFDDitBEARBEAQhashhJQiCIAiCIETN/weSkZLOiNBiiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 展示4个特征，temp_2,temp_1,actual,friend\n",
    "\n",
    "#设置布局\n",
    "fig,((ax1,ax2),(ax3,ax4)) = plt.subplots(nrows = 2,ncols = 2,figsize = (10,10))\n",
    "fig.autofmt_xdate(rotation = 45)\n",
    "\n",
    "#标签值\n",
    "ax1.plot(dates,df['actual'])\n",
    "ax1.set_xlabel('');ax1.set_ylabel('Temperature');ax1.set_title('Max Temp')\n",
    "\n",
    "#昨天温度\n",
    "ax2.plot(dates,df['temp_1'])\n",
    "ax2.set_xlabel('');ax2.set_ylabel('Temperature');ax2.set_title('Previous Max Temp')\n",
    "\n",
    "#前天温度\n",
    "ax3.plot(dates,df['temp_2'])\n",
    "ax3.set_xlabel('');ax3.set_ylabel('Temperature');ax3.set_title('Two days Prior Max Temp')\n",
    "\n",
    "#朋友猜测的温度\n",
    "ax4.plot(dates,df['friend'])\n",
    "ax4.set_xlabel('');ax4.set_ylabel('Temperature');ax4.set_title('Friend estimate Temp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.切分数据**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = df['actual']\n",
    "labels = np.array(labels)\n",
    "\n",
    "\n",
    "features = df.drop('actual',axis = 1)\n",
    "\n",
    "feature_list = list(features.columns)\n",
    "\n",
    "features = np.array(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练特征: (261, 14)\n",
      "训练标签: (261,)\n",
      "测试特征: (87, 14)\n",
      "测试标签: (87,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_features,test_features,train_labels,test_labels = train_test_split(features,labels,test_size = 0.25,random_state = 0)\n",
    "\n",
    "print('训练特征:',train_features.shape)\n",
    "print('训练标签:',train_labels.shape)\n",
    "print('测试特征:',test_features.shape)\n",
    "print('测试标签:',test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4.建立随机森林模型**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测结果的误差: 4.920974926768583\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "#先建立1000棵树的RF模型\n",
    "\n",
    "RF = RandomForestRegressor(n_estimators = 1000,random_state = 0)\n",
    "RF.fit(train_features,train_labels)\n",
    "predictions = RF.predict(test_features)\n",
    "\n",
    "#评价指标MSE\n",
    "from sklearn.metrics import mean_squared_error\n",
    "result = mean_squared_error(test_labels,predictions)\n",
    "RMSE = np.sqrt(result)\n",
    "print('预测结果的误差:',RMSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5.树模型的可视化**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[WinError 2] \"dot\" not found in path.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pydot.py\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(self, prog, format, encoding)\u001b[0m\n\u001b[0;32m   1914\u001b[0m                 \u001b[0marguments\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marguments\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1915\u001b[1;33m                 \u001b[0mworking_dir\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtmp_dir\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1916\u001b[0m             )\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pydot.py\u001b[0m in \u001b[0;36mcall_graphviz\u001b[1;34m(program, arguments, working_dir, **kwargs)\u001b[0m\n\u001b[0;32m    135\u001b[0m         \u001b[0mstdout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msubprocess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPIPE\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 136\u001b[1;33m         \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    137\u001b[0m     )\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\subprocess.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, encoding, errors)\u001b[0m\n\u001b[0;32m    728\u001b[0m                                 \u001b[0merrread\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrwrite\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 729\u001b[1;33m                                 restore_signals, start_new_session)\n\u001b[0m\u001b[0;32m    730\u001b[0m         \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\subprocess.py\u001b[0m in \u001b[0;36m_execute_child\u001b[1;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, unused_restore_signals, unused_start_new_session)\u001b[0m\n\u001b[0;32m   1016\u001b[0m                                          \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfspath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcwd\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcwd\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1017\u001b[1;33m                                          startupinfo)\n\u001b[0m\u001b[0;32m   1018\u001b[0m             \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 2] 系统找不到指定的文件。",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-71e9b33661c4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;31m#展示\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0mgraph\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite_png\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'tree.png'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pydot.py\u001b[0m in \u001b[0;36mnew_method\u001b[1;34m(path, f, prog, encoding)\u001b[0m\n\u001b[0;32m   1732\u001b[0m                 self.write(\n\u001b[0;32m   1733\u001b[0m                     \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprog\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mprog\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1734\u001b[1;33m                     encoding=encoding)\n\u001b[0m\u001b[0;32m   1735\u001b[0m             \u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'write_{fmt}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfmt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfrmt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1736\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pydot.py\u001b[0m in \u001b[0;36mwrite\u001b[1;34m(self, path, prog, format, encoding)\u001b[0m\n\u001b[0;32m   1815\u001b[0m                 \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1816\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1817\u001b[1;33m             \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1818\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'wb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1819\u001b[0m                 \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pydot.py\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(self, prog, format, encoding)\u001b[0m\n\u001b[0;32m   1920\u001b[0m                 args[1] = '\"{prog}\" not found in path.'.format(\n\u001b[0;32m   1921\u001b[0m                     prog=prog)\n\u001b[1;32m-> 1922\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1923\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1924\u001b[0m                 \u001b[1;32mraise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 2] \"dot\" not found in path."
     ]
    }
   ],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "import pydot\n",
    "\n",
    "#从随机森林中随机选择一棵树\n",
    "tree = RF.estimators_[5]\n",
    "#导出dot文件\n",
    "export_graphviz(tree,out_file = 'tree.dot',feature_names = feature_list,rounded = True,precision = 1)\n",
    "\n",
    "#绘图\n",
    "(graph,) = pydot.graph_from_dot_file('tree.dot')\n",
    "\n",
    "#展示\n",
    "graph.write_png('tree.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#把模型以及要用到的特征输入进来，转化为dot_data类型\n",
    "from sklearn import tree\n",
    "\n",
    "tree1 = RF.estimators_[5]\n",
    "dot_data = \\\n",
    "    tree.export_graphviz(\n",
    "        tree1,\n",
    "        out_file = None,\n",
    "        feature_names = feature_list,\n",
    "        filled = True,\n",
    "        impurity = False,\n",
    "        rounded = True\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pydotplus\n",
    "graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "graph.get_nodes()[7].set_fillcolor(\"#FFF2DD\")\n",
    "from IPython.display import Image\n",
    "Image(graph.create_png())\n",
    "\n",
    "graph.write_png('tree.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#限制下树模型\n",
    "\n",
    "small_RF = RandomForestRegressor(n_estimators = 10,max_depth = 3,random_state = 0)\n",
    "small_RF.fit(train_features,train_labels)\n",
    "\n",
    "tree2 = small_RF.estimators_[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = \\\n",
    "    tree.export_graphviz(\n",
    "        tree2,\n",
    "        out_file = None,\n",
    "        feature_names = feature_list,\n",
    "        filled = True,\n",
    "        impurity = False,\n",
    "        rounded = True\n",
    "    )\n",
    "\n",
    "import pydotplus\n",
    "graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "graph.get_nodes()[7].set_fillcolor(\"#FFF2DD\")\n",
    "from IPython.display import Image\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6.特征的重要性**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.0109045 , 0.01945424, 0.02204945, 0.51800532,\n",
       "       0.38901991, 0.02113454, 0.00219089, 0.00655828, 0.00212262,\n",
       "       0.00229808, 0.00160136, 0.0019517 , 0.00270913])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "importances = list(RF.feature_importances_)\n",
    "feature_importances  = [(feature,round(importance,2)) for feature,importance in zip(feature_list,importances)]\n",
    "\n",
    "feature_importances  = sorted(feature_importances,key = lambda x:x[1],reverse = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('temp_1', 0.52),\n",
       " ('average', 0.39),\n",
       " ('day', 0.02),\n",
       " ('temp_2', 0.02),\n",
       " ('friend', 0.02)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable:temp_1               Importance :0.52\n",
      "Variable:average              Importance :0.39\n",
      "Variable:day                  Importance :0.02\n",
      "Variable:temp_2               Importance :0.02\n",
      "Variable:friend               Importance :0.02\n",
      "Variable:month                Importance :0.01\n",
      "Variable:week_Mon             Importance :0.01\n",
      "Variable:year                 Importance :0.0\n",
      "Variable:week_Fri             Importance :0.0\n",
      "Variable:week_Sat             Importance :0.0\n",
      "Variable:week_Sun             Importance :0.0\n",
      "Variable:week_Thurs           Importance :0.0\n",
      "Variable:week_Tues            Importance :0.0\n",
      "Variable:week_Wed             Importance :0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[print('Variable:{:20} Importance :{}'.format(*pair)) for pair in feature_importances]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Variable Importances')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_values = list(range(len(importances)))\n",
    "#绘图\n",
    "plt.bar(x_values,importances)\n",
    "plt.xticks(x_values,feature_list,rotation = 90)\n",
    "plt.xlabel('Variable');plt.ylabel('Importance');plt.title('Variable Importances')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "RF_most_important = RandomForestRegressor(n_estimators = 1000,random_state = 0)\n",
    "\n",
    "#选取两个最重要的特征\n",
    "important_indices = [feature_list.index('temp_1'),feature_list.index('average')]\n",
    "\n",
    "RF_most_important.fit(train_features[:,important_indices],train_labels)\n",
    "predictions = RF_most_important.predict(test_features[:,important_indices])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.144075730064745"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 预测结果\n",
    "\n",
    "RMSE = np.sqrt(mean_squared_error(predictions,test_labels))\n",
    "RMSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从均方误差来看，其值没有下降，反而上升了，说明其他特征还是有价值的，不能只凭特征的重要性就否定部分特征数据，一切都需要试验验证。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对比真实数据与预测数据\n",
    "\n",
    "years = features[:,feature_list.index('year')]\n",
    "months = features[:,feature_list.index('month')]\n",
    "days = features[:,feature_list.index('day')]\n",
    "dates = [str(int(year)) + '-' + str(int(month)) +'-'+ str(int(day)) for year,month,day in zip(years,months,days)]\n",
    "dates = [datetime.datetime.strptime(date,'%Y-%m-%d') for date in dates]\n",
    "#创建一个表格，用于保存日期和真实值\n",
    "true_data = pd.DataFrame(data = {'date':dates,'actual':labels})\n",
    "\n",
    "#创建一个表格，用于保存预测值\n",
    "test_years = test_features[:,feature_list.index('year')]\n",
    "test_months = test_features[:,feature_list.index('month')]\n",
    "test_days = test_features[:,feature_list.index('day')]\n",
    "test_dates = [str(int(year)) + '-' + str(int(month)) +'-'+ str(int(day)) for year,month,day in zip(test_years,test_months,test_days)]\n",
    "test_dates = [datetime.datetime.strptime(date,'%Y-%m-%d') for date in test_dates]\n",
    "\n",
    "#创建随机森林模型\n",
    "RF = RandomForestRegressor(n_estimators = 1000,random_state = 0)\n",
    "RF.fit(train_features,train_labels)\n",
    "predictions = RF.predict(test_features)\n",
    "\n",
    "test_temp = pd.DataFrame(data = {'test_date':test_dates,'pred_temp':predictions})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  actual\n",
       "0 2016-01-01      45\n",
       "1 2016-01-02      44\n",
       "2 2016-01-03      41\n",
       "3 2016-01-04      40\n",
       "4 2016-01-05      44"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_data.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_date</th>\n",
       "      <th>pred_temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>48.514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-02-24</td>\n",
       "      <td>57.942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-13</td>\n",
       "      <td>62.561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-02-17</td>\n",
       "      <td>54.867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>61.918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   test_date  pred_temp\n",
       "0 2016-01-07     48.514\n",
       "1 2016-02-24     57.942\n",
       "2 2016-10-13     62.561\n",
       "3 2016-02-17     54.867\n",
       "4 2016-11-08     61.918"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_temp.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Temp changes with date')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r1qzBrbfeiqioKBQVFeHs2bP49ddf8cUXX9jMWG8Mhg0bhrFjx2L06NFITU3FqVOnkJeXB4VCITghTIiRI0di1qxZyMnJQY8ePTBs2DCkpqairKwMJ0+eRGpqqluzradPn44pU6Zg4MCBGD16NMLCwnDq1CmcOnUK9957L7Zv397gY/N06tQJc+bMwRtvvIE77rgDo0ePRnh4OPbt2wedToeMjAxcuHDBxt3r2rUrVqxYgaeffho9evTAwIED0bZtWxgMBly9ehXHjh2DVCrFr7/+CgDYvXs3lixZgp49eyI9PR2xsbEoKioyO6tPPfVUncfbr18/vPvuuygrK3Mo2dSvXz98++235n/Xlbvvvhu///477r//fgwYMAAhISFo27Ytxo4dW+dj1JUVK1Zg2LBhmDNnDnbv3o2OHTvijz/+wNdff40hQ4Zg9+7dNtvfcccd6Nq1K7Zu3YqCggLcfvvtKCoqwrfffovu3bvb3GTw9O/fHwzDYPny5SgsLERsbCwA4IknnoBMJsP8+fNx/vx5vPnmm9ixYwf69u2LxMRElJWV4eLFizh+/DjmzJmDTp06efz9E0RThQQrQTQzWrVqhYMHD5q/LHNzc1FTU4P4+HhkZGTgkUcewT//+c9GH8f48eMxYcIErFixAjt27IBUKsW4ceOQlZVV56YBAFfvtXfv3ti4cSO+/fZbVFdXIy4uDl26dHEow1VfRo0ahU2bNmHlypX45JNPIBaL0aNHD+zatQs//vijRwQrAMyfPx9paWl488038eGHHyIyMhKDBw/GwoULcffdd4NhGJtyTgDw4IMPonv37li7dq05ZymTyZCYmIgRI0Zg9OjR5m2HDRuG69ev49ixY9i5cydUKhXi4+PRt29fTJ8+vV6lp+666y4wDAOWZR1EqfXP9RGsvNO4e/durFixAjU1NRgyZEijCNZOnTrh22+/xcKFC3HkyBEcOnQIXbt2xSeffIILFy44CFaJRIJPP/0UixYtwv79+3Hq1CmkpKRg2rRpePrpp3HLLbc4vEbXrl2xbt06rFu3Du+//z40Gg0Art6vTCZDSEgItm7dio8//hgfffQRdu3aherqasTGxiI1NRXPP/+8YKcvgmjOMBUVFY5BHoIgiEZi3rx5WLNmDTZt2iRYVJ2wcOPGDaSnp6N169aCj6sJgiCaC5RhJQiC8DFlZWUO5b/0ej1efPFFmEwmEvYEQTR7KBJAEAThY3Jzc7Fq1Sr069cPSUlJuH79Og4fPozLly8jIyOjXhlTgiCIQIQEK0EQhI/p1asXjh07hu+//x43btwAy7JIS0vDnDlzMHv27Fpr0hIEQQQ6lGElCIIgCIIg/BrKsBIEQRAEQRB+DQlWgiAIgiAIwq8hweoG+fn5vh5Cs4TOu++gc+876Nz7HvoMfAede9/hL+eeBCtBEARBEATh15BgJQiCIAiCIPwaEqwEQRAEQRCEX0OClSAIgiAIgvBrqHEAQRAEQRBew2g0orq6ul77SKVSVFZWNtKICFd48tyHhYVBLG6Y9CTBShAEQRCEVzAajVCpVJDL5WAYps77hYSEQCqVNuLICGd46tyzLIuKigpEREQ0SLRSJIAgCIIgCK9QXV1db7FKBAYMw0Aul9fbXechwUoQBEEQhNcgsdp8ceezJ8FKEARBEARB+DUkWAmCIAiCIAi/hgQrQRAEQRAE4deQYCUIgiAIgmiiHDp0CHK5HDdu3PD1UBoVEqwEQRAEQRCNyJIlS9C7d29fD6NJQ4KVIIhmzc2bDB55RObrYRAEQRAuIMFKEESzRqUCioroTyFBEK7Zu3cvhg0bhpYtW6JVq1YYO3Yszp8/b15fVFSExx57DK1bt0ZiYiLuvPNOHDx4EJs3b8ayZcvw22+/QS6XQy6XY/PmzQAAuVyOL7/80uZ1unTpgtWrV5t/XrNmDfr06YOkpCTccsstePLJJ1FRUeGdN+1HUKcrgiCaNXo9g+pqqgtJEIRrqqur8fjjj6Nz587QaDR4/fXXMWHCBBw/fhwGgwEjRoxAXFwcPvzwQyQlJeHMmTMAgLFjx+K3337D7t27sWPHDgBAZGRknV9XJBJhyZIlaNWqFQoKCvD888/j+eefx1tvvdUo79NfIcFKEESzRq8HGth4hSAID/HeexK8916w0/UmUyhEIs/dWD70kB4PPWSo1z6jRo2y+Xnt2rVITU3FqVOncOHCBZSWlmLPnj2IiYkBALRu3dq8bVhYGMRiMRQKRb3HOmPGDPO/W7ZsiezsbEyaNAkbNmyASNR8ng6RYCUIollDDitB+J6HHjK4FJBardYj/ezd4dKlS/i///s/nDx5Ejdu3IDJZILJZMLVq1fxyy+/oFOnTmax6kny8vKwYsUKXLhwAUqlEjU1NdDr9SgpKUFiYqLHX89faT7SnCAIQgDOYSXBShCEayZMmIDr169j5cqV2Lt3Lw4ePAixWAy9Xg+WZRt0TIZhHPY1Go3mf//1118YP348MjIy8N577+HAgQNYs2YNAECv1zf8zTRBSLASBNGs0emAqiqggd83BEE0A27evInz58/j6aefRv/+/dG+fXuoVCqzuOzWrRvOnTvntBZqcHAwampqHJbHxsaiuLjY/HNpaanNzz/99BP0ej2WLFmCHj16oF27digqKvLwu2sakGAlCKJZYzAwYFkGGo2vR0IQhL8il8sRExOD999/HxcvXsThw4fx9NNPQyzmkpXjxo1DbGwsHnjgAXz//fe4fPkyvv76axw8eBAAkJaWhoKCApw+fRo3btyATqcDANx1113YuHEjfvrpJ/z888+YMWOGTfShbdu2MJlMWLduHS5fvoxPPvkEGzZs8P4J8AN8KlhVKhVefPFFdO7cGQkJCRg8eDB+/PFH83qWZbFkyRJ06NABCQkJGDFiBH777TcfjpggiEDj7+8NqNUUCyAIQhiRSIR33nkH586dQ+/evfHcc8/h5ZdfRkhICABuUtXOnTuRmJiICRMmoHfv3liyZAkYhvu7ct999+Gee+7BqFGj0LZtW3zyyScAgMWLF6NVq1YYOXIkpkyZgsmTJyM2Ntb8up07d8bSpUuxbt069OrVC++//z4WLVrk/RPgBzAVFRU+exD28MMP49y5c3jjjTeQnJyMrVu3Yv369Th27BiSkpKwcuVKvP7661i7di3S09Px6quv4tixY/jhhx8QERHhq2Gbyc/PR3p6uq+H0eyg8+47AvHcf/mlGFOmhOH0aSVatfLfXEAgnvumBn0G7lNZWYmoqKh67+cPk66aK54+9w29BnzmsGo0Gnz11VdYsGAB+vbtizZt2mDu3Llo3bo13nnnHbAsi/Xr1+Opp57CqFGj0LFjR6xfvx5VVVXmOxOCIAh30ek4B4QmXhEEQfgvPhOsRqMRNTU1DqpdJpPh6NGjuHLlCkpKSjBgwACbdX369MHx48e9PVyCIAIUvR5gGJYEaxPhs88kyMsL8vUwCILwMj6rwxoREYEePXrg9ddfxy233AKFQoFPPvkEJ06cQJs2bVBSUgIAiIuLs9kvLi7O5Qy5/Pz8Rh23r1+P4KDz7jsC7dxfvRqHiIhkXLhwDdHRSl8PxyWBdu4bwoEDyUhJ0SEp6bpPXp8+A/eQSqXm3Gd90Wq1Hh4NUVc8ee6VSiVKS0sdltcWt/Fp44A333wTM2fORMeOHREUFIRu3bph3Lhx+Pnnn83b8IFlHpZlHZZZ4818EeWZfAOdd98RiOdeLg9GTAyDqKhkpKfXvwuNtwjEc98Q9HoZIiJqkJ4e7fXXps/AfSorKxuUh6QMq+/w9LmPjIxEampqvffzaZWA1q1b4+uvv0ZhYSHOnTuH/fv3w2AwoGXLlub2ZfYq/Pr16w6uK0EQREPR64EWLSgS0FQoLWWg0dBnRRDNDb+owxoWFoaEhARUVFRg3759GD58uFm0fvfdd+bttFotjh49ip49e/pwtARBBBJ6PYPoaBKsTYWSEhHUal+PgiAIb+PTSMC+fftgMpmQnp6OS5cuYf78+UhPT8cDDzwAhmEwffp0vPHGG0hPT0e7du3w+uuvIywsDOPGjfPlsAmiSXDlCoOWLf23TJO/oNPxDquvR0LUhdJShmrmEkQzxKeCValUYuHChbh27Rqio6Nx3333Yd68eZBIJACA2bNnQ6PR4LnnnkNFRQVuvfVWfPbZZ35Rg5Ug/BmWBSZODMP331f5eih+j8EAyOUsqqpIBPk7RiNQXk5dyQiiOeJTwTpmzBiMGTPG6XqGYTB37lzMnTvXi6MiiKZPTQ3VFa0rOh2DFi1IsDYFrl9nkJjIksNKEM0Qv8iwEgThWfR6gCrA1A2DAX9nWH09EqI2SkoYtGploklXRMCzevVqdOnSxfzzkiVL0Lt3b7eOuXnzZiQnJ7s7NJ9BgpUgAhC9HvSlXkd4h5Ucaf+nrEyEVq1MNOmKaHY8+eST2LlzZ523l8vl+PLLL22WjR07FqdPn/b00LwGCVaCCEAMBoYc1jqi13MOK0UC/B/eYaVIACHJzUVEly6IjI5GRJcukOTm+npIDuj1eo8dKzw8HC1atHDrGDKZrEmXBSXBShABiF7PlWuqqfH1SPwfqsPqeVatCm6U45aWitCypYkmXTVzJLm5kM2aBVFBARiWhaigALJZsxpdtI4YMQJz5szBCy+8gJYtW6Jly5aYP38+TCYTAKBLly5YsmQJZs6cibS0NDz22GMAgGvXruGRRx4x73P//ffjzz//tDn2qlWrkJGRgeTkZEybNg1VVbYTZoUiAVu2bEGfPn0QHx+P9PR0TJ8+3TwOAJgyZQrkcrn5Z6FIwLvvvovu3bsjLi4O3bt3x6ZNm2zWy+VyfPDBB5gyZQqSkpLQrVs3bN261WabZcuWoXPnzoiPj0dGRgamTZtW73NbF0iwEkQAYjBw/yeXtXYsdVh9PZLAYcOGhrXerI2SEgaJiSaYTHRz0ZyRZmeDsbtrYTQaSLOzG/21c3NzYTKZsGfPHqxcuRKbNm3CunXrzOvXrVuHjIwMHDhwAFlZWVCr1bj33nsREhKCnTt3Ys+ePVAoFBg1ahTUf2dbPv/8cyxevBhz585FXl4e0tPTbY4pxLvvvos5c+Zg0qRJOHLkCHJzc3HLLbcAgLl+fU5ODs6fP29Tz96a7du347nnnsP06dNx9OhRPP7443jmmWfwzTff2Gy3fPlyDB8+HIcPH8bYsWPxxBNP4K+//gIAfPnll1izZg3eeOMNnDp1Clu3bsWtt97asJNbCz6tEkAQROOg13Nf6Fotg7AwqsXqCr0eCAtjYTCQCPIEej0nLFkWcNFFu0GUljJQKOh6bu4wV6/Wa7knUSgUePXVV8EwDDIyMvDHH39g3bp1eOKJJwAAffr0wezZs83bf/DBB2BZFuvWrTO3lV+5ciXatWuH3bt3Y8yYMVi/fj0mTpyIhx9+GADw7LPP4tChQ7h48aLTcbz22muYPn26+XUB4B//+AcAIDY2FgAQFRVl7hoqxJo1azB+/HhMnToVANCuXTucPn0aq1atwrBhw8zbjRs3DuPHjwcAvPzyy9iwYQOOHj2KtLQ0FBQUQKFQYMCAAZBIJEhNTUX37t3rfkLrATmsBBGA8NEpenRaO3o9g+BgEkGeoqyMgcnUOHGUkhIR4uNNnj8w0aRgU1LqtdyT3HbbbWbhCQA9evTAtWvXoFQqAcBBrP3888+4cuUKUlJSkJycjOTkZKSlpaGiogKXLl0CAJw/fx633367zX72P1tTVlaGa9euoV+/fm69l/Pnzzt0Du3duzd+//13m2UdO3Y0/1ssFiMmJgZlZWUAgNGjR0Or1aJbt2544okn8MUXX0Cn07k1LmeQw0oQAQjvFmq1DAASY67Q64HgYK7ZAuE+paWcD6LXA2IPf8MolQyobwyhzcqCbNYsm1gAK5NBm5Xlw1FxhIWF2fxsMpnQpUsXvPPOOw7bRkdHN+g1WA/+sWIEHoPYLxPb/SIzDGMeQ0pKCk6ePIm8vDwcOHAA8+bNw7Jly7B3716Hc+Eu5LASRADSmA5roOViecFKeIaSEu7LzoMTpG3gv0vpBqP5YsjMhCYnB6bUVLAMA1NqKjQ5OTBkZjb6a586dcpGMP7www9ITExEZFmPiCUAACAASURBVGSk4PbdunXDxYsX0aJFC7Rp08bmP16wtm/fHidPnrTZz/5na+Lj45GUlIS8vDyn20gkEtTU8pijffv2OHbsmM2yo0ePokOHDi73s0cqlWLIkCFYsmQJ9u/fj99++w3Hjx+v1zHqAglWgghAeLHAOayeo7AwGE8+KfPoMX2N0chALAbEYhZGo69H0/QpLWUwEZuR1MezJYeMRkAi4YSCVMqikZ46Ek0EQ2YmVGfOQFleDtWZM14RqwBQXFyMF198Efn5+fjyyy+Rk5ODGTNmON0+MzMT8fHxmDRpEg4fPozLly/jyJEjePnll82VAh5//HF89NFH2LRpE/78808sX74cp06dcjmOZ555BuvXr8fatWvxxx9/4JdffsHq1avN69PS0pCXl4eSkhJUVFQIHuPJJ5/E1q1b8fbbb+PPP//Em2++idzcXMyaNavO52Pz5s14//33ce7cOVy+fBmbN2+GRCJBmzZt6nyMukKRAIIIQPhIgKcdVrU6KGDLP4WFAdXVQFSUr0fStFHs24a3MROSa9wMaObvkkMA3BIVajUQGsr9Wybj2rNKpWSzEt4lMzMTJpMJAwcOBMMwmDx5skvBGhoaiq+//hqvvPIKHnroISiVSiQkJKBv376Qy+UAuIL+ly9fxqJFi6DRaDBs2DDMmDEDW7ZscXrcRx99FBKJBGvXrsUrr7yC6Oho3HPPPeb1ixcvxssvv4xOnTohMTERZ86ccTjGyJEj8eqrr2L16tWYO3cuUlNT8cYbb9hMuKqNqKgorFq1CvPmzYPRaET79u3xwQcfoFWrVnU+Rl1hKioq6De+geTn5yM9Pd3Xw2h20HmvnV27xJgwIQzbtlVj8GDP2YZffFGILVvSsW1b4LQa6tcvHHl5VZgwIRTLl2uQlOSffxKbynVvTOmKmKq/HJabUlOhEvjSrCvFxQxmzZJh2zY1Jk0KxauvapCS4t3Pqql8Bv5MZWUlohpwV6jVaiGVShthRHVnxIgR6NixI1577TWfjsPbePrcN/QaoEgAQQQgBgP32NTTDqvBIGq0bKKvCQ+n5gGeILqqQHC5uyWHNBoGsr/TKKGhLLUeJohmBglWgghADAYGkZGsxzOsej0TsPVK+UgA4R4lwamCy90tOaRWc1EAAJDJuJ8Jgmg+UIaVIAIQvR5/C1bPHtdgEJm7aAUaYWEslMrAFOPeJEexGK9cexwhNRZF6YmSQxqNpQkGn2ElCG+yc+dOXw+hWUMOK0EEILxg9fRjU85h9egh/YbOnWtw+nSQr4fR5PmmxSR8cNc6aOI9W3KIc1i5f4eFUSSAIJobJFgJIgAxGLgC6552WPV6kbnta6AxYIAR+/ZJfD2MJk1VFZcvPd1xAvb971ePlhxSqxmKBBBEM4YEK0EEII3psAZSrVKjkau/CgAJCSxUKk50EQ2jrEyE+HgWwcGsx7POGg1jU9aKHNamiyc7NRFNC3c+exKsBBGAGAycYPV0cXWDgQmoKgH2Xa769q3B4cMU7W8opaUM4uNNkEg83+mKq8PKfdmFhpLD2lQJCwtDRUUFidZmCMuyqKioaHDLVvrLTBABiF7PNJLDKgqoKgH2gnXgQAO2b5dg6NAAspG9SEkJA4WCEyKeFqxcWSvLpKvKysC5DpsTYrEYERERUCqV9dpPqVQ6bX9KNC6ePPcREREQixsmPUmwEkQAwkcCios9+xDFYAisSVd6PYPgYIvT06tXDRYs8F1x8v/8R4Z16zQ2ItpfqagA5s2TYc0aS7Hf4mIRFAoTyss9X/5MowH+bgyEsDDPX9uE9xCLxfUuHF9aWorUVOGSaUTj4i/nnn7jCSIA4SMBnm4cwDmsnj2mL9HpbB3W4GCgpsZ3zt1vvwWhqqppOIfXr4vQ5thWRHTpgsjoaER06YKY3dvQunXjRAKqq2nSFUE0Z8hhJYgAhIsEmKhxQC0YDLYOq6/RaLhJXy1a+HoktSP7PBcv/vkkRCynHJmCAkwqnIHrgw04H/yAx29s7CddUR1WgmhekMNKEAFIYzUOCHSH1ddoNEyTaQ/b6q1XIGNtbU6pSY3E1dmQSFiPlz+zn3Tl6acHBEH4NyRYCSIAMRiAqCjPT7oK9CoBvkatbjqCVVZ2VXC5qPAqgoMbd9JVaCg5rATR3CDBShABCBcJaJzGASYTg5oazx7XV+j1DEJC/CcSoFYD1dW+HkXdqGqRIricTUlBcDA87sRzDiv3b74Oa0UFcPUqCVeCaA6QYCWIAMRg4GZSe/qxrF7PIDycDZhYgF4PSPykuZXBwDnYTWXS1Xf3LEQ1Qm2W6cSh0GZlNVIkwNph5QTs//4Xgnfe8SOLnCCIRoMEK0EEII3lHBoMgSZY/cdh5TOZTSUScKLdRDyGt2BMTgXLMFBGp2Lv/WtgyMxstEhAWJilDqtazWDfPjFKSuhrjCCaA/SbThABSGM5h3q96G/B2jREVW34k8PK542bimBVqRh8GjwJl747C2V5OeZPzoch834AaJRIgEbDlbMCOIe1tJRBYaEIpaVN43wRBOEeJFgJIgAxGDjR4Onuh3q9CGFhnhcjvkKnA0JCfD0KDt5hraryzetLcnNtaqpKcnNdbq9UAgoFaxbYFy+K0Lo1F24Wiz0fCdDpGPMEuaAgoKBAhNGjDbh+nQQrQTQHSLASRACi1zOQSDz/qJvPsAZKpQB/qsNaXc1ALjf5xGGV5OZCNmsWRAUFYFgWooICyGbNcilaVSoGiYkms8C+epVBSgp3LhvDYQUAxurUyGQshgwx+LTRA0EQ3oMEK0EEILzDCnjWZeUzrEZjYIgEoTqsDON5Z7ouaDQM4uJYnwhWaXY2GLvCpoxGA2l2ttN9OMFqKS9VU8OAbxHOZVgb93107GjC7bfXgGEAk6lRX4ogCD+ABCtBBCB8fVGJxLMTpLhIQCA5rI6CNSiI9YkAUquB2FjWJ2WtmKvCNVWdLQcApZJBQgLnCGs0lqL+AP6uEuDxYdrw9dfVkEiAmBgTbt4MjBsogiCcQ4KVIAIQo5FBUBAglXq2IxDLcscMnAyrYyRALAaMRu+PhXdYfVHWik1xXlPVGWo1g9hYFlVVQEUFA7ncch4bKxJgDZ+53bM/BMl3dK41c0sQRNOGBCtBBCgMw+X8tFrPCiCJBAFVJcDRYfWNYFWrGcTF+SbDqs3KAstPwf8bViaDNivL6T4MA4SHcxGG8nIG0dHWgtWzk66MRs755rHO3IrAQlpSe+aWIIimDQlWgghgPO2wApwYCRSHVagOq68cVrUaf2dYvf/ahsxMaHJyYErlaqqaUlOhycmBITPT6T4syzWn4AWrtcMqkXi2Dqt1lyugYZlbgiCaNmJfD4AgiMbD4rB6bhaRWOxcjGg03KPgyEjXxygr4x5/+xqhOqxiMfv3zHPvjs+Xk64ATrTaC9TKSiAqynFbluUdVqCgAAIOq2cjARoNY5ORbUjmliCIpg05rAQRwEilgFbr2WMGBzt3IHfskOC991y3yjSZgH/9K8yzg2oger1jHVbfZViBqCjP1y91hzvvjBCsC8s5nizCwrjMrb1g9XRrVuumAUDDMrcEQTRtSLASRAAjlTZGhtW5GKmsZGoVKlVVno8pNBSherW+yrBWVzOQyXzvOvOwLHDtGoPDhx0fxCmVDCIjWYSGco5wRYWjw+rJc6hW256bhmRuCYJo2pBgJYgARibzvMPKTboSXqdSMaipcb2/SsX4RBAKIdTpypdVAsLC/EewVlVxwnPfPkfBqlIxiIhgER4OJ5OuPJ1htT039pnb4pC0WjO3BEE0bSjDShABjFTKmnvUu4vRCIhErgWrUglz8XhnKJWM31QZEKrD6qsMq1pt+9jb15SXM+jb14hjx4QFa2Qk+/ekK0AkshWsnEvtuc9Y6NzwmVuWBYb0C8fBTB/1tCUIwiuQw0oQAYZ1lybOYfWMcODcSNPfzQiEj1lXh7W2bbyFv9VhlclYiESsX5yf8nIGCgWLpCQTLl2y/apQKhlERMCcYbWPBDAevh/hz40QvupMRhCEdyHBShABhtEIcy4zLIzFhg3BeOklqdvH1em4vKerkkVKJSPorGk0wObN3HR8f4oEaLWOkQBfZVi5blFAWBh8UtrKHl6EDhxoxIEDti6rUglERHAOq1rtGAnwNPy5cUZICIviYv9w7QmCaBxIsBJEgGFdqmnyZD1WrdI4CI6GoNMBwcEmlxNqlEph97S4WITNm4PN2/iDYGVZ4MKFILRsaduH1VcOKz/piq9t6mt4EZqcbELid1sR0aULIqOjEdGlCxL2b/tbsHLiuqKCiwg0Fmq1bVkre155RYuJE0NRVub780YQRONAgpUgAgyDwSJYZTKgQwcTJBL3H5vqdJxzKxY7rxLgzD3lRQ23Df7OiPqW/HwR2rQxCdRhhU8eyfe5/DEyBnfG17tD0Ppu37caLS8XITrahLbHt2LM1zMhKigAw7IQFRTg7i0z8c/fP4ZEwmVVTSauFXBjYV8lwJ4776zBK69oMWFCKG7c8P21RRCE5/GZYK2pqcHixYvRtWtXKBQKdO3aFYsXL4bR6tuOZVksWbIEHTp0QEJCAkaMGIHffvvNV0MmiCaBXu+Yy+TKW3niuCaXReGd5VP5meQAP+nKvbF4gr17xRg40HEgYjHr0QlDdUGSm4tnLzwOcSHXajSk2PetRvnuVV0+egXBRrXNOolBg15fLfDaWGqLBABAv341eOklHSZMCDVfawRBBA4+E6wrV67Exo0bsWzZMpw4cQJLly7F22+/jeXLl5u3WbVqFdauXYtly5Zh//79iIuLw5gxY6BSqXw1bILwe4S6N3ENBNz7Eucd1tqqBAg7rLaC1R8iAfv3izFggONAfJFhlWZnQ2ayFYW+bjXKRwJk14W7R4Xd5JYbDEBQUOPOeqrNYeUZONCIZ5/lRGtFRaMOiSAIL+MzwXrixAkMHToUw4YNQ8uWLTF8+HAMGzYMp06dAsC5q+vXr8dTTz2FUaNGoWPHjli/fj2qqqrwySef+GrYBOH3GAyMQ6kmrryVe8flHVZXjQO4SIDjuqoqTjBrNP4x6UqjAcrKREhLcxRBvsiw+kOrUZa1jY1UVHAOqy5euHsUv5xl0aAJV/WJqNTFYeUZMsSIWbN0mDgxDEplvYdFEISf4rM6rL169cL//vc/XLhwARkZGfj9999x6NAhzJkzBwBw5coVlJSUYMCAAeZ9ZDIZ+vTpg+PHj+Phhx8WPG5+fr5Xxu+r1yM46Lw759IlKbTaROTnXzIvq6lpg/PnC1FdrWvwcf/8MxzBwdFosWsDnvpqBSL/ew16hQKFM2bg5rBhAACV6p8oL1ciP/+yzb4XL8YAaI2ffrqMwsIkiEQxOH8+HyIf3TKfOxeGVq1ikZ9/xWGdSpWCK1cqoVB470lOF4UCIcXFDsv1CoXNtd6Y1/2qVSkYNOgmOnXinN6CgraorCzAT5kz0X3dQkhNljserUiGXybMgCg/H0FBHSAW62yuNwDQ6W7BhQv5TktcffihAvHxegweXF7r2AoL03Dz5g3k59etfEJGBjBqVDRGj47HihX5CA011b5THaG/Pb6Dzr3v8Ma5T09Pd7neZ4L1qaeeQlVVFXr27ImgoCAYjUY8++yz+M9//gMAKCkpAQDExcXZ7BcXF4eioiKnx63tDXuS/Px8r74ewUHn3TU6nQgxMSE25yg2VgaFohXS0xv+xX31qhh3XX0PQ36YD7GOEy8hxcVovWQJEhITobo3EzIZg7CwSIfPJzycs3zl8jYApGjRAmjdOt3BCfYWRUVBSEmRCF5HcXEhSEiIQHq692zWmkWLoJk6GzLWEgtgZTLULFpkHmNjX/cMI0NEhOV9Gwxh6N69JcpSZmD9sQTMLpkP5upVsCkpWB65GJkzRyM+nkVMjBRpaY7nMjw8BK1bpzvEU3gMBil+/JHBzJmxtY7tjitf4IGX5yO4hHt9bVZWrV2t0tOB2NggzJ/fFR99VI2wsLqdB1fQ3x7fQefed/jLufdZJOCzzz7Dxx9/jI0bNyIvLw8bNmzAxo0b8f7779tsx9jdnrMs67CMIAgLXCSAhSQ311yKaPX2dETucG8Cj04HZP60xCxWefispUrFoEULk9MqAQkJJpSXM1CpgBYtWJ9OvNLpGEilws+kfZFhNWRmIjtlA9dqFAwq5alebzWq1XIdpXiqqhiEhwPBwSz2xk+C6swZKMvLoTpzBl+FTzKXsQoNZSGXO55LV/V6AS4acuCAuNaKDJLcXEw7OQMhxZYqBXWdkDZunAGTJunx73+Huh2JIQjCt/hMsGZlZeGJJ57Av/71L3Tq1AkTJkzAzJkzsWLFCgCAQqEAAJSWltrsd/36dQfXlSAIC3o9cMeVjyGbNctciii2+i+0f+MJt2ad63RATPU1wXXM1atQqbhJOsIZVgYpKbxg5bKRvsyxCjUM4PFFhtVoBL6NmwjVmTP4YJMKbzx5watiFeC6Sdm38WUY7jzpdI7b8udvyI0tmLcxw1yjlb/GXFWTALjyZhkZJpw+7boeljQ7G1I3JqRNmGDA2LEGPPhgqNuVMgiC8B0+E6xqtRpBdoX7goKCYDJxjyxbtmwJhUKB7777zrxeq9Xi6NGj6Nmzp1fHShBNCb0eGPNDFhg7S0msc2/WuV7PoDwiSXAdm5ICpZKbfOOsrBUvWPV6bsa3L2uxunJYfSFY1WquZi7gu05XWi0EXcjgYBY6neNnxTCc+/n4qRmIqnB0P4ODnU/OAziHdcwYA/budZ1M88SEtMmTDRg+3IgpU0Jdur4EQfgvPhOsQ4cOxcqVK7F7925cuXIF27dvx9q1azFy5EgAXBRg+vTpWLlyJb766iv8+uuvmDFjBsLCwjBu3DhfDZsg/B6DgUGLqgLBde7MOtfpgF19n0dNiMxmOSuTQZuVBaWSd1gd962uZpCczJqbB3AF5xs8lHqzf7+tKHLtsHpfTGs0DMLCLO10VSrvi3mNhjF32NJoYBb0ISGOj/b5VJY0OxshNcLuZ22RAKWSwX33GWrtwsamCFcpcLbcGQ8/rMfAgUb85z+hbjfR8AQ//yyizlwEUQ98JlhfffVV3HfffXjmmWfQs2dPzJs3D1OmTMH8+fPN28yePRszZszAc889h7vvvhvFxcX47LPPEBER4athE4Tfo9cDFZGe+ZK3PS6Ds93uw58vrkZZaBpYhoEp1ZK15CMBwg4rkJxsQkUFA4bxfk706aelNj/7W4ZVo7E4rOHhvmnNyjms3OvyJa0AR8Fqspq358r9DA5mYTA4fx96PYOEBBZqtev3qp6XBQ1jW9OKv0mqL1Oncm/k3DnfN3n86KNgnDzZiO3BCCLA8FmVgIiICCxduhRLly51ug3DMJg7dy7mzp3rxZERRNNGrwf2DsjGuN0zbGIBBokMhgZ8yfPodEBICAvVyPsx98xkvPOO7fNjpdJ5NpXPsJ4/L4FIxEIsdp1v9DTXr4tsWtbqdHA6a9w3kQBLYfzISN85rPzlYi1Y7ee4VlfD7AazKSlgChzdfDYlBcHBrh3WunJjSCbebx+Cl6tfNlcpqEuVAGdkZurx6acSdO7c8BJvnqC8nHG7mQdBNCd8f5tJEIRHMRgY/P7P8dDk5HCzzhkGyuhU7B631q2JPFynKxPEYmHnzOKwOq5TqxmkprIoKOBmnnvzsTvLchlRPo4A+GOGlUFoKC9YOfHvbbgqAdzr8l2uhFCpGEREcOu0WVlgZcIRkdoiAXWlvFyEHzuMt6lS4M51fM89RuzZI/F5LKCigqHKBQRRD0iwEkSAwbdmNWRmmr/kt736O053nODWcXU6rlyWM+fMUiXAcR0fCfjrLxEiI1mvikKDATCZGJv+8v6WYVWrLZ2cwsNZ+KL7tFbLmMta2QtWlrWcD6WSQVQUt86QmWlzY2QdEXEVCTCZAIbhjsHdADkflyvx3BCkUqBbtxqcOOHbx/HksBJE/SDBShABhsEAJ61Z3fty5ISwCcHBwmKTn3QllGE1GhnExLAoLBQhIsK7gpV3sawFq07HCRchfJNhtUQCJBL4pIKCRlN/hxWwvTGydj9dOaxVVUB4OPfv0FDh6gQ8nhasAFef9ZNPnHQ08BLl5eSwEkR9IMFKEAGGXs9AIrH9gpfJ4HYNSt5hFYuFyxWpVHDqsAKcEAwJYb0uWHkXy9ZhZRAS4l+TrkKt5hX56nE1f1NTXs4INgMAHAWrM1xlWJVKyzFCQ11PvGoMwdq3rxFHjoh9WguYHFaCqB8kWAkiwNDrG8dh1elgjgQIfdHzDQFcuYPR0ezfkQDhBgONQX0dVl9kWKurLQ6rL2BZW6dTpWLMnazsUSq5nG1tuIoEWItemcz1telKPDcUsRi4804j8vJ8M++YZSnDShD1hQQrQQQYQpEATzisnHNrcvqoV6l0LnJ4LILVuw4rw7A2k65cOaxiMWptF+ppNBrLpCtuDN7tBKbTAXK5pZyW/WfJMKzZ9bV2R13hKhKgUllysDKZbUtYexpDsAK+jQVUVXHXGDmsBFF3SLASRIAhFAnwhMOq1QLBwaa/xabjsZwJGS77yi2PjmYREeHdx+5aLRAXx9Yjw+o995fHPhIQEQGvlrbSarnPxtphtf4sJRJLGbLGiAS4ujYrKjwfCQCA22+vwZkzQT5xOcvLGcTFsdQqliDqAQlWgggwhCIBnnFYuce89nU5edRqRrC2qVptqXnKCVbWq52uNBoGiYmsQ4Y1ONi/ylpZRwIiIlgold57fb7TFl8NwF6UBgdzIp9fV5uTzu1TWySA+3doqOtWtI2RYQW4+rKDBxvw7bfejwWUl3PXpLs3kQTRnCDBShB+Rk0N8OefDf/VbKwqAfykK1cIidmqKkvbUbnc+xlWrZZBYqLJrg6rf2VY7R1WTzUPkOTmIqJLF0RGRyOsUxdo/pcLgLv5KCqyFfDW50Olss2phoSw0OkscYH6RgKMRuDSJcs1zR3fMcNqPd6ILl0gyc1tNMEK8LGA4No3dIM//nD8Xa6o4K5JTzms+fn0VU4EPnSVE4Sf8ccfIkydKqt9QyfodM6qBHimrJUznM1sr65mEB7OrXzgAQNuvbXG62WtkpJMda4S4IsMq/2kK85hde/zkuTmQjZrFkQFBWBYFuLCArR4YTYkubn49lsxnnvOco1ptbBppGAvSu0d1roKVj5GcPasCAsXWgrfCkUC7McrKiiAbNYs9Mj/uNEEa8eOJhQWMqisbJTDAwAefTTUwS0vL2eQlGTyiMNaUwMMGRLm1c5xBOELSLAShJ+h1QI//hiEGzca9mWm01n60vNIpe7n5TiR51ywOosKVFdbHNbu3WsQG8t6NcOq0zlGAlw7rL7IsNpOuoqIcN9hlWZn27TmBYBgoxrS7GxcuiRCXp7YLHK0WlvBbDQy5ja2AHf98KXMVCogKqr215dILPtUVDA2Dre1YOUnXQmNl9FoMO2veQ7XsycZOdKInTsbb/JVaSnjIEzLy0VITPRMhvX06SDcvCmiigNEwEOClSD8DJ2OAcMABw40LFsn5B5Kpe47rDodnLqSrqiqgkO21dsOa0SEbZ6Sm5gmvL0/1GGNinJfsDJXrzpdfvGiCK1amfDDD0Hm1+cFvJBTbu2w1jUSYD3pqrxchPJy60iAJQfLO6zOxpto+AuS3NxaX6+hjB2rx6efNo5gNZl4wWq7nMuwmjxSJWDfPjGCgliqOEAEPCRYCcLP0GqBXr1qsHdvwwQrJyxtlzlzP+uDXs9ALBYWKjU1gEgkvI6bjGW7TiLxXvtTe/eQx9k58UUkQHjSlXvnh01Jcbr84sUgPPaYDvv2cdcYf45kMmHXLySk/pGA4GBLJKC83LY1rvUxQkM5h9XZeBkAslmzGk20tmrFNS4oLfX89XjzJoP7a7ag/ZDONrlcLsPqGYf1u+/E6NGjhhxWIuAhwUoQfoZOx6BnTyN+/jmoQR2PXOUz3cWZyBMSyTzWGVaeoCB4LXPHu4cMwzleteErwepY1sq9Y2qzssDaPUvXiEJR/lwWbt5kMHq0wVw4nz9HMhnn7NrffISEWB7v63SM08/aGutIQHm5YyTAetKVWs0IjpeH0Wggzc6u2xtvAKNHG/DFF553WWs+yMXbmAppiW0ut8OpjxEba3L7pq2igrumk5M949YShD9DgpUg/AytlnOd2revwe+/1/9X1FU+s7FwJWKqq30bCeBmwHPVCepSKsrTGdZ164JrfV2NBjYOq32VgNxcCWbMyMALLwh/sKtXBzu4dYbMTGhycmBKTQXLMLgmScM7vdbhdMcJCA5mERnJuaB8i1CZjEVoKPcImy85xRMcXP+yaNaubHk5A6WSsarlaqlCwEcC+PE6u9VyFhnwBGPGGPD5554XrCnrFiIMtl0RGI0G//oxyyMTyY4cEaNvXyOkUpDDSgQ8JFgJws/Q6TiBpVCwuHmz/sKpsRxWe7fX+mf7WebWWJe14nEmWIXKGrkLJwa5GrDWLp8zPJ1h3b1bgitXXP+ptRf8EREsKistYz14UIxHH72Go0eFYyI7d0psHrnzGDIzoTpzBpU3yzHslosoHjAeBw6I0bIlZzWnpppQXMyYHdbQUBYlJSKHOqvWDmtdiYmxXL/l5QwUCpP5PalUFtfdutOVITMTbGqq4PGcRQY8QXw8F4e4csWzLqWsTFhkx2oKPCJYb95kkJDAjZ0vO0YQgQoJVoLwM7iOUpxDVV+RADh3WBsSL3CGRGLbOrS2SID1DHh+f/vHoc7KGrkrWnmHNTqaNU/8cZXp9bT7W10NlJaKahXj1mOyrxJQWsqgZUudy0oMrh4JV1ZyNXDbtDFh3z4xWrfmBCt3ThjzOZLJgJISx4wqP+nKZHKeVbYnPt6EkhLufJeXM2jd2lJaTK9nzLWC7TtdCUUDWJkM2qysOr1uQxk71oDPPvNsTdbKKGGRXSxJRXi4+8fnzTf8dgAAIABJREFUbnTYvydVun88gvBnSLAShJ+h13PiITiYNT9Sre/+9o0DAL7laMPHZS2W7PvE866wENXVcPhyFnIxnZU1cje7qNXyDisnmGoT7p7OsFZXM4jYvs2lGLcfk30koKyMgVxuENwW4FxkV4KltFSE+HgTev75ET462havvRGKiC5d0P/aRygvZ2wyrCUlIgfBKpVyn7dK5fhZOiMujkVZmcVhbdXKJOhw85OueGyiDGBQEZUKTU4ODJmZdXvhBnLvvQZs3+7Zrldf9FgEbVCozTJWJsPqhMUemQjJPdnwTGMQgvB3SLAShJ+h1XJupXUGsL4IfRm648LYiyTrovCAZcwA58BZT26yrsPKIxY7TrpyVYbJHTQaa4eVgV7vujyX5x1WBrd/vqBeYty+SoDRyEAs5txItdpxe43GtcNaUsJgeMVH6LD8SbTEX2DAieax38xA7LfbzA4rFwlwbL3K3Twxda4QAHDXG/+YurqaQUqKxWG1vj75SVfW8FGG1auq8O6C840uVgGutmxSEotff/Xc1+I30RPx8YB1qJRzOWJTKie+v2kxybyNO08++O5znmi9TBD+DglWgvAzeLfSuh2mJ+BKFjXseFy7V8s3q0RiW9fU2mG1F3zOMqz2LqarMkzuYHFY+cffzuMLAO9Ee+68V1cDUZX1E+MREVz9WoB7DM8LPGctW9Vq15NuSktFGPPDfIjsNpIYNOjxxQLzOQoNhaBg5W+e6iNYeViWGz9//u1LoNlHAqxpzLasQowb59marCUlIlwffD9WP3MeyvJyqM6cgereTPPvCt/QQ6ezfWJRV8hhJZoTJFgJws/gBZV1sXZP4M5MYnuR58phtResQnVYuW1sv2AbK7vIO6xyOSeYXMUXhMbvLgYDgzKZ84lERiMc6tsGBQEmk+VxeosWnGXtrD5rXRxWZ6I5qvKq+RxZIgG22/AOa2Ulg6iougtI68oMvGC9ccNWhIaGOr8uy8tFiI6uQy0yDzF4sNFc6ssTcPVWTTYO8qVLIiQnc++Jb+ixeXMwtmypv1Dmr2VPtF4mCH+HBCtB+Bn8pCnrdpiewB2H1V7kucqw2udTeRfIGq50lO0y+zJM/ONTdx8H868fHc2i7fGtaNmvM3btCXFahcCTGVajkZt8tD51sVMxzlcxsId/VFxayiA+nvtBqGVrTQ0nVlw7rAzUscJO9Y2wVAeH1d5F5R3WigoGcnndBatCYcKVKyLIZJZIxqVLIrRpYxGhEgmcPkkoL6/f67lLaKjzsTSU8HDbyMO+fWL0789d/JwzCly/zjSosxl/o8gfhyACGRKsBOFn8GWp/NlhDQ62fWxuvZ5zBy3b8u6dNc5cTD67yD8+9UR2kX/99B+2YtJ3MxBcVAARnFch8KTDWl0NJCSw+Fw2CZqcHFwNSgMLWzHONQ1wLspKSxkoFNz6yEjHhgL8Z+raYRWhYPoCB9FcI5Xhg1uyHRxWZ2Wt6vuIPj6exfnzQZDLLYL14kWRuUoB4Lpig7cjAQDf8MD94xgM3I2ZddkuANi/X4yBA7kLjL+JLC9nHHK8dcHisFJrViLwIcFKEH4G77ByZa3qt6+rCRyedFjFYucOq33hfd69s8a7jQO412/99kKE1DgWcZc9/rhNqSlPZlj5Ll8sC1SOyERqzRVsfKvKRow7c1iDgznhFP5VLhZuSsetPXpgwXvpiP5mm812fHbR1c1IWRmDoMmODvaNJTn4KnySlcPKoqrKucNaXwGpUJhw/rwI0dGsuQ6uvWB1hS8Ea2Iii6Ii9z//sjIGcXGcmOQ/I7Wae0/JyXyGlfvc+EoN9cXisFLjACLwIcFKEH6GxWGt/6Qr+8lR1njWYa17hlWvh0OZLaEMa2NhMjEICgIkxU4mPtXU2JSaivgq14MOKydYg4NZXLjA/bm1/wycOawRESxMH+bizvdnQl7JlcOKrixA73eesHGFeffO1c3I9esMYmJYBwdb/GAmKisZs8PKt4cVEqx6ff0FpLXDymeIL1+2jQS4gpvkVeeX8whJSSYUFrr/1cg749YZ3SNHxLjjDkvexH2Hlfu9bndiK17c0N6jDTcIwt8gwUoQfoY7DqurGfDcjGRPZVidVwkQqrFq/9jX3ZqwDcGUXHu1AUajQeSybI9lWPm2tPHxLI4dE2NW7AeYsqCDjbDQaJwJViD6tWwEGxxn9luXw1KrGYhElgzjnj1ic/mo778PwvPPS1FVxYl2e/jJXbzDyreHdVbWqqKiIYKVc1itBWurVraClWGEj8lXGPAmKSkmXLvm/ldjSYkIcXEmhIayqK7m3oR1HACw3ERWVDANmuWv1TKI27MV/1j7BFqo/vJoww2C8DdIsBKEn2HtsNZXYLqaAe9OrUZ7IWxfR9U+w1qb4JNIvBcJ4NEtyIKGCa11O1HhVY+NjS/ppVCYIMndhmXljyOqwrZ5QNTObYKRgK5dayApqr0clkbDiUj+Wtm2TYKzZ7k/7Tt2SJCebsKHHwoUb7XCUoeV+1morFXDHFYT/vyTE6whIVzFBO61bLfzdDtcd0hOZlFY6L5K/usvEdLSTDZlu65cESE93ZnDWv/X0OmA+JXZCNJ5vuEGQfgbJFgJws9oTIe1obUa7YVwcLDrDKt921V7PN1NyhW8e2fIzMTClA2oiuE6KLFCliM4J7a28dcVvmlCfDyLzJ+yIBXI0HZ4/xWzs2nNwIFGXA91Xg6LR60GWrRgzTcjVVUMSku5P+2lpQz69TOiQwfnj+CDgjgHkO90BTh2s+JvnuqfYeWuBX6fqioIlsWyn5gE8O1+vZtfBbhIwNWr7n01SnJz8fDCDnhidjhS7uyCvgUfAXAU/LzDWlnZsEiAVstAXIebGoIIBEiwEoSfwTusUmn9M6zeclglEudVAuoyoYpzaL0/q/lgykS898rvyH6lGpoNGwRLTekWeK5nPZ9hVShYJJsKBLcJvX7V7Gxa061bDV6VL3ZwhXVBoTa1aTUaLp/K34xUVzMoKeH+XVLCtWR1hVzOTYYSibhJV6GhLMR2pUj5m6f6lrWKjWUhErFmkSaTQTC/KtQ8oL7xA0+RnOxeJECSmwvZrFmIqeIe0QddLcBLlx6HJDf377yyZVveYQ0Obli+XKdzHnVxt+EGQfgbJFgJws/gHdaGlLXylsNqHwlwlWFlWcfX9FaG1b5qglzOorhYxD2ebqS6r9ZUVfEZVhOuMsJuaVWLFMEMq0gE/HXneDwnf9M8Rl1CKtZ1X2czRrUaf0cCLD+XlnLnvKKCQVSU6zFGR7PmrlqhoY5xAICvEsDAaGQgqUd9+6AgTrTywjM6mkWbNo7WOidYbZd5uwYrj7tVAqTZ2Q5teGWs2vyI3jqTK5UCN24wiI83Neh3s6aGgW5BFkxSzzfcIAh/gwQrQfgZBgPXN74hkQCdjnH6GNWdbjgN7XTF16K0x1tlreybFkRHsygutmQoG6PuqzV8ly+FgsX61MWoERAWB4csdFqHdcAAI75vNQGqM2dw6sQJ/HXwLL6WT7LZRq1m0KKFvcNq+dNe26QlPl8KcI6fUOtVrk1wbe9WmPh4W8EqVNKKiwTYDtQXJa0AvgJGwwWrs0fxQsulUhZFRSIoFG60Tc7MRPmyHBSHpDXajRdB+AMNFqzV1dUwmbzXMo8gmjJKJfDgg7VP+OFhGDSorFXtDmu9DmeGd315uElTjOB668YBGo1jlyt+f29kWPnJRDycYBV5LRvJZ1hbtzZB9O9MFGbloFRmKyx+6jhBcNIVAAwaZMTIkRZlL9TpSqNhbDKsGg3nsOp0zkucWSOXs+ZzFBoKDB3qeCcREsJdW85m87ti6FCDOZbQq5cR3boJO6y8YD10KAhZWVKfCVbAPYHu7FF8TVKKQ1ZZJgOKirjPz1UN5Vpfc1ImRnT6s9FuvAjCH6iXYD1z5gzGjx+P5ORkpKWl4dChQwCAGzduYPLkyThy5EijDJIgmjo3bzI4eVJ4ko8z+GLt9cFVhlUqrb9jy8PnannEYtbOYbWst24coNUyghOKvOWw2gtme4e1samu5iYwxcayeP55HfSZ9+OBO/6wERZqtfA5Arj9nnnGchFwk3RsBataDcTEWB4pR0Rwj5nLyiwtXV0RHc2aBbNIBCxa5Bh0Dg5mUVYmQmRkXd+5hXnzdAgL4/49Y4YerVq5nnS1Y4cEX3whwc2bvhOs7sQCtFlZMIbY3oFomFAUz8pyiDhIpSyuXRO5/T7tbyAJIhCps2D98ccfMXjwYJw9exYjR44Ea3U7GBMTg7KyMmzatKlRBkkQTR2VisG1a6J6OZyedlhDQtypw+rosNpmWG0dVl6MOnNYg4JYr0y6shfM3nZY+bJWPDIZ6/DoW6OBWdDVhtDjfXuHVSTiaquWloqgUNT+FMzaYXVGSAhQXNx4AtJ60tXRo2J06FCDkyeDfJJhBdxrHmDIzMSezLVQRluy0YvTNuDPnuMdzh/vsPpKmBNEU6LOv5HZ2dlo2bIlTpw4gf/+9782ghUA7rrrLvzww/+z9+bxUVT5+v9T1WtlX8gCJIFEWkUFdxEX0KCD27hdA8p1wx1EdEYdR6+CROc61+XrXFzHBcdxQCTu8xs3DAzqHUVQRDKABJJAFsjaSTpJ731+fxyqu6q7qrt6I51w3q8XL5Ku6q5T3elTTz3ns2xK+AAZjNGAuIzb1KT9IhiLI0odVvXXi3WZM9hhDa6jKndYA9tSzWHNySFobz+UDqu8KYAgKHe6UnNYtRBIuuL8McM6HUFbm3aHNdL7YTCIMaXJCQMTk66amjgUF/tw0UVufPSRYdiEXElJfN2uPsubh3Urdvid9K9Kr8H+/aFOqslE/I8f6gYJDMZIQ/M3cvPmzbjuuuuQnp4OTuGbVVJSgvb29oQOjsEYLfT3c9DrCRoatF8Eg2udaiFc7cp4HFaXK9hhlTuk0u3SxgGpEMMa7LB6POqJaYlGDAkQETtLSYlWsHJcIEZYfL5YJUDsrDVmDMH27ToUFWkNCQi/H8fRzyxZAlJMulq3zoBZszyorPTAao1/qTxWxo+ngj9WGhp4lJcH/sDT0pSX/sXzzs31gedJVN8Jrxfg+cDrMcHLGO1EdQtpCFPPpKOjAya1tUgG4zDHZuMwebIvomCVLlzwvHJJqHAEL91LSbTDKhXTwTGsYuH94KQnkeGMYQXU36NEIyZdhcNuh2IdVjXS0wkGB6XPp7VeCeH8jm5hIUFdnS5iDVZAm8MK0LCA5AlWgtpaPVatMuC88zwoKyM46ijvsAnWeEICAKClhUNJidxZb2sLXfoXvxs5OUSxeUI4wn3XGYzRiOZv5NSpU7F27VrFbR6PB++99x5OPfXUhA2MwRhN2Gwcjj/ei8bG8F85lyu+7j7BwlJKomNYpYJTLYZV7FEfDK3DOhwxrFTADVcMqxLROqzBlQLs9sB7HGhU4ENdHa8pJGDSJB8eeCByRwmTiSQtpvRXv/Jg5kwP7rrL6W8s8MYbQ4olsA4F48fH1+3K5+NkzRcEgaC1VdlhBahgVWqeEA6Xi67CSImn0gCDkepo/kb+5je/QW1tLe677z7s2rULANDT04Ovv/4aV1xxBerr6/Gb3/wmaQNlMEYy/f20c1EkhzU4aSraC9Chc1jlIQEeD/wXaKl7arcPv8MqfT9FwXWonCmpmAy3j9akK4C2Nu3vD7z3tHsSPS/q6NLapw0NOk1JV3o9cPzxkfdLpsNaXExw/fVuXHaZx7+0fcwxPqh0z006xcW0mkSsBC/Pp6crC1bxuyFWaojGYQ1evYinFBeDMRLQLFjPP/98LF++HGvWrMFFF10EALj55ptx2WWX4aeffsKLL76I6dOnJ22gDMZIxmbjMHGiD93d4b9y4cpSaeFQOazBna6kSGNYqcMZus+haxwgdy+zsmjc36FyWAmhLU9DHw/8HK/DOjQUCCmgnbWI31ktKEjceRqN5LDJZqcx1rF/V4Lr3woCcPIvb+OCOyYjKzcXmVOm0BauB78bubnRO6zBN7dmc+ytlxmMkYA+8i4Brr32Wlx66aVYu3Yt9uzZA5/Ph/LycsyePRs5OTnJGiODMeLp7+eQlUVgMBDFpTyRcGWptJBsh1V8fnBIgBS9PjjpKnViWHleLOMU/nk8T+DzQVFsxgvtHx9wXul7q/35oSEBAcErdtYqLPQhM5NE5dxGIpkOaypCb/Cid+MHBriQbmHTG9/GZR13QgC1ULnmZgiLFyPnaUCnuxmZmfLmCVoIvrkVBCp4h6sUGIORbDQJVofDgT//+c844YQTMHPmTPzHf/xHssfFYIwq+vvpRWzCBB/27eMxaZLyEmwyHVbqisbnsEoFq/S1pMlhWhoHxJJQFgtKx6etSMO/x2IcrtqNRTyIwkI6rmgyvLOyaIiJCA27oF2o+vvFGFaiKeEqGozGw0uw0uYBfNRxtPS7Ln/som+WIB3y9X7ObkfmE9XIy7sJHIcYQgKUHFYOwOHzGTEOLzT5B2azGU888QT27t2b7PEwGKMSmy0gWMPVYg2+CHFcdHGs4RzWaF9LPi5OtswZ3OlKipbGAYDyWJYtM8XcPlYJpzM0hvS443wR40pjcYB/9zv5iRKi3MpUECDL8o/2M8nKIujrk94s0BsAQaAdrtLTgeJiH6ZOTWzdsCOOGL6s/eGAJl5Ff1PV308/IynZfS2K+/KtLTj1VPo5BYcELF1qhs2mfpzgm1ux9bKhpgaZU6bIQg8OJx5/3CS7oWOMHjQveB1zzDFMsDIYMWKz0ZCAzEy5WAkm+CJEk5u0HyecwxoPkTpdSZGKPaczuvjMb7/Vy8RYvEgTkkTefHMoonMqFd1a8HqBmhp52T+1CglSYaIWMhGOggIfurpCp26zmaCri4YEZGYCb7yRQOUPYMUKe0ShP5oYP96HtrboY0LEm1PZY7klivuSkhKsWkVt1bQ0eVOJv/9dj/Z27Te3ZjOQ9f/VQFi8GHxzMzhCwB8MPTicROt33+nR08OK0o5GNH8bH3nkEbzxxhvYsGFDQg48ZcoU5OTkhPybM2eOf5/XXnsNU6dORVFREWbOnIl//etfCTk2g3GosdloAXnqgqhPpsEXIaMxurjTZNVmpA0JAr9LY1iDnURaHJ/+HM5hVcLlQlRxfJGgNU6jF/A0Dlf7OPr7OVitvKzwu1oN1rS0wNJvUxOPiROjW3IuLKTduoIxm0WH9fBxQZPJ+PEkplqsYviPlB/+YxmGOHmxXSIIcCxZ4v9d2rbX4wH27eNhtar/DSo5rOWvPgouaImCs9thrq6O+jxGKg4HjSNmjD40J129/vrryMvLwxVXXIHy8nKUl5fDHHQl4jgOb731lqbXW79+PbyS2f3AgQM455xzcPnllwMA3n//ffz+97/HM888g9NPPx2vvfYaqqqq8N1336G0tFTrsBmMlMDn46DTASdsX42zPlmKzAUtICUlcCxZAndVlX+/4IuQyUTgcmmPS4vksMbaDYcQTvZcvV4cV6iYDY5hVXMQlcbidHJRxfFFYnBQuUpBJOg5aN9fFBb9/YHi8DRjP3RfMYYVEDsiRSdYi4oIOjpC3zxBIOjq4mWdtRixM368D998E31dLXE1Rcr+c+fgD1/q8Zj3v8C1KH/3pUlXLS0cPB4Ovb3ab24FgUDoUg494FqUHx+N2O20gQZj9KFZsH7//ffgOA4FBQUYGBjAtm3bQvZRatmqxpgxY2S/v/XWW8jMzPQL1hdeeAHz5s3DDTfcAAB46qmnUFtbixUrVmDp0qWaj8NgpAIcR2PLzq9ZDIOLOiBipjAA/4VLyWGNplTNoep+YzQGQgKCx6zTBVzhaLOs3W5EVdonEsHJTVqJNoZVFBZWa0CwqjusAWHS0MD7C+VrpbDQ518qppUMArVl9+9nDmuiGD8+tm5XSiEBaWnAxiOuhu29S1WfJwiA2N28oUEHs5lE6bACg3klyOhuDtmXlCiHJIxGaItiJlhHI5oFq9gsIBkQQvDWW29h7ty5SEtLg8vlwk8//YS77rpLtl9lZSU2btyYtHEwGMmCEMBcXQ3epbxcJwpWpYtQIh1WcSzx9h2XhgQEj1lL4wBxHMEk2mGNtii/iFoMq88HdHRwKC6WD14UFlKBoSZYpdngTU08pk2LIkgZNLREvCAPDQXiZAWBsJCABFJUpBx6EYn+fi7kJiQtLXINW+q8U4Hc0MBj6lRvWMGq5LBuumIZzll5pywsIDj0YLTjcHAYGBjuUTCSQVR1WJPF+vXrsXfvXlx33XUAgO7ubni9XhQUFMj2KygoQEdHR9jXqq+vT9o4U+F4DMpIet9dLg4+31Gqy3JcS4v/fPbuzcfAgA719fTv3Oksx65d++HxaLNZe3uPRnPzbnR2Kl8cfb6jsX377pDC5pFwOif7x1hfX4/WViOs1lLU1+9BW5sRbjf9GQA6O/PQ1WVAfX07OjvL0dGxH/X1oeOXvqaI3T4Ve/bsx7hxfVGNT43OziNw4MA++HzRiUKHowK7d7fA4XDJHv/lFwFvvVWMxx9vlD2+Y0cejMaJqKtrQ1ZW/8F9s+B0ZqK+vlW279BQERob3aiv70FdnQVz5zaivl6bnSu+X+J719OjByHlqK+vh91egvb2fHR2NqK+3hXhlRhacLuPxg8/NCArKxC+Fmnu2bt3PMaMsaG+PpCqrtPpcM456bLHguntzUJrK/17+fHHElRUcNizx4P6+v2K+zc3F0CvJ6iv7wIA2GyFWD/2HEx48EGMf/FFGNvb4SoqQuvCheg54QRgBM2ZamiZ9wcGjkdjYzvq63sOwYgOHw7FNddisYTdrlmwdnZ2atovWGRq4c0338RJJ52EqVOnyh4PDjEghEQMO4h0womkvr7+kB6PQRlp73t3N4eCAhNISQm4ZuXlOvF8cnKMyMkBLJZsAMCYMQKKi42wWLQtG3OcgGOPnaRa8D4nx4ySkknIzo7uHEwmMywWi/+9T0vjYDIJsFgsIITHmDEm/znU1Rng9XKwWLKg16fhqKMMKCsLFchmsznkc/R69cjJGQ+LpTC6AarAcek49tiJiLavSU6OgLKychxxhPx9P3BAB4/HFDLuf/7TiIoKgrS0ElgsVBxv367HxIk6WCzyZJuyMiN0OgKLJR9DQxk49dRyTY639O8+J8eE8eMtMBo5jBlDP4dx40zo79fjuOMmJrTD1eHM3Lk6bN16NG66id4AaJl7DAYzJk/OhMVSJHv8lFMAoEjxOQDQ1aVDXZ0BFksarNY0XHmlG5s362CxZMBQUwNzdTWdPw62krPklGLzlctgsVwJACgrM8Dp5JB/62I4Fi+GeIuYf/DfSEfrvO9265GRMRYWy2g469QgVa65mgXrkUceqSlGtacnuruazs5OfPLJJ3j66af9j+Xn50On04W4qV1dXTEJYgZjOKFdrgDHkiUwLVoMnVN9uc7hoL3iRQJJV9oQa3KqYTYTOJ3RFRdXWrqXhgSExrASeDy8f1s0SU9ud+JDAmJLulIOCaDVAEI/D6uVQ3m5T7atry80+QagS7dWKweXi5YtiyU8o7CQJl45HIGyXWYzTY5jIQGJY84cN264Ic0vWLWgVCVAC9IqAW1tPI491ou1a/W0hevixYFl/oPJytm9zZj5tzvhmu6Fu6oKZjPQl5iFiRELIfQ7H650IGPkolmwPvPMMyGC1ev1Yu/evaipqcG4ceP8S/rRsHLlSphMJlx55ZX+x4xGI0444QSsX7/en4QF0NCBSy9VD1pnMFKR/n7aTtNdVYWmJg6Ff6pG/lCzSpUAeZJStGWtImEyRd9v3OOhwkqKtNNVrDGsSjidiU26cjq5mLpVqcWw2mzaBavNxmHMGOWkq7Y2Hvv28Sgri60bVVGRDx0dPAyGgCA3mwk4jhxWtVKTTVERQXY2QX09r3mVI1bBKtZh9fmo8MrLI+jt5aizqtJNQ++ygz8YA09L5iWhl/AIwuWiN22srNXoRLNgvemmm1S33XfffTj33HOjPjghBH/9619x5ZVXIjOol92dd96J22+/HSeffDKmTZuGFStW4MCBA5g/f37Ux2EwhhNp1vDApXPw6K7r8Oqryhcgp5ODyRS4MJpMoiOaGGJxWIMdVEDe6UqpSoBYsS6cw0qIPAGMEHrBSWSnK46LzcFUc1jDCdaTT/Zizx5etq9SySpRmDQ2Rl8hQESsxZqbS/wOqyDQhKx4E+oYcq65xoW33zZgyRJtd45KZa20IFaP2L+fw9ixPuTkUCc+UkkqcbsgJPbmdiRit9OqGaxKwOgkIbdj2dnZuP766/Hcc89F9byvv/4aDQ0N/tJVUq688ko88cQTeOqpp3D22Wfju+++w5o1a1BWVpaIITMYhwwaEiAu2wZqcP74ow4bN8rrPAY7rCZTdBehSGIlFofV5Qp1SaUhAcHbadF9+rPXy0Gvclus0xF/gwGAvh4hia2hSEhsr6XXE8XGAf39HPr6OFmDAIAK1ooKn6xupvRzlyIu/e7eHX0NVpGiIh86O3kMDARCAMxmwsIBksAFF3jw2WeGkM9cjcHB2CpTiDcyu3fTGxmzmd7ARipJJW5PSyOw2Q5voeZwcMjPJywkYJSSsPUDk8mE1tbWyDtKmDFjBnp7e3HyyScrbr/llluwbds2dHR0YMOGDTjzzDMTMVQG45AidVgFISAYN2/WYfNmuWANLktlMlHXMVEEHFbtKDmsBkNgXMHbpY0DwiEVvUDg9RIZEhAr6g4rvSno75ePsbeXCtbgkAClpWFRmHzzjR7Tp2tUQUGIDuv33+twwgn0NQQBTLAmAbMZmD7dg6++0rYgSQgXNo5cDfFGhv5d0D8+jqOx70RlmcJrCsTAH3ecF1u2RN/oYDThcABjxjCHdbSSEMG6a9cuvPrqqymRRcZgpBrSJUKzmcDhoJPp4CAXIh5DY1gTGxIQi8MaHKMKiO1XlWNY1eI/gwnez+WiCUSJDAnTQwyeAAAgAElEQVSIlXAxrOPGhRZ0t9s5FBfLHVabDcjKCn0NQSDo6+PQ1MSHVCHQiph0tWGDHjNm0IFShzWml2NEYN48N1atMiT1GGYz/TvasEGPmTMDf3yuq6pgX74cvtJSEABEpwMB0JlWhj2/f84fA5+dTW8Cu7oOX7Fmt1OHlcWwjk40x7CedtppilUC+vr60NHRAUEQ8Le//S2hg2MwRgPSQuJSh3VwMDSjX8lh1RoS4PFQdzMciXJYgUD4QajDqk2wBu/ncgE5OYFM6XjR8n5oHZtIfz+HCRN8inGs9LON7LAKAsHXX+vx61+7Y443LSz0oa5OB0EIiGLmsCaPk07yYtcuHQYGkpfUxHE0tKS0lPjLzmVmEnrjU1UlS84EgNvnC3jkMieAwE1PZaUH69frUVUVXd3h0YLDwWHMGB96eg7v5LPRimbBetxxx4UIVo7jkJOTg/LycsydOzek3SqDwaBOm5hTKM2uHxjgQoRgaAyr9rJWasJSSqIc1nDbqfsa+XUDoQPk4OtQwZooh1XaBSpawsWwlpXJnVS1zmFq2eLp6UBnJ49Zs6Lo/RpEYSHBpk16LFkS+DDNZoKMDCZYkwHHAZdf7kZtbR5OPFF9P/odjP0zsNup6BTJzaVuvlIstMPBhTQAOe88N156yXTYCla7nYYEKJS7ZowCNAvWFStWJHMcDMaoRc1po3FW8seDHVajkQpeLUQSlkBiHVbp9tzcwO+iO6lUv1VKsIvpdnPIzk5c/JndHqhRGi3i2PwF21taQEpKcLr5D3CcUBXUgjXgbEorH7hcoTckAHVY9XqCs8+OXbCaTEBOjg+VlQFhIggs6SqZzJ3rwrXX5uO++wJ3Y1Yr5/8bFrP8Y6kQICIIkN3IiIK1pIRAFxSeGnxzCwBTp/qwbZsOPl/4eszhoCsTsT03EYhlvYLPVwsOB4eMDOI3BRijC81/0vfeey+2bNmiuv2nn37Cvffem5BBMRijCbUC8oODocv9TqdcHNKQAG2Tr92eHIfV4QgvhIO308YBHBwOhH1esGB1OmnThEQlXcXaNACgF8tx/3wHwuLF4JubwRECvrkZD+y+A+e0vS0TrFYr5+8Tn5FBIvYxT0sDfvc7p2J8azT85jdOTJ0aEE8lJUTmzjESy7hxBEajDw0N9LJZV8ejsjIdCxYIWLBAwAUXZMRcg1XkuutcOP74QCJeTg5B+kc18JZORVZuLjKnTIH53nuROWUK1q4zYcLM42CoqfHvz/OAxeKTlVeLhu5uDtdemxZ5xyTy6ad6PPtshIlMBTrnJHhAjJRB81/1ihUrsHv3btXtDQ0NeOONNxIyKAZjNNHbGxA0UgYHOVnMIyDWYZV3utIaw+pwcBCE8BfLaASwSKSldbtdfpEQhSgVsurPU4thTVxIQOT3I9zYpq5+NKRgu0CGcNYnS0MEa04OPY7oiIWD56lgjZe773bJXLQxYwiuv/7wXAo+VFxySTfefpsmX61da8ADDzjx7rtDePfdIZSW+vDTTzoElRSPit/9zin7TGe0vI3jX1iEgqF9/psm4+uvg29uBg8CfWszhMWLZaLVYvH6RXW0dHVxIRUwDjUtLXzMiWNa5kDGyCVhkclWqxWmSPYOg3EYIhU0UgYGuBAx6nbTOFeRaMpaaXNYSdQOa6Sl9VCHlS7rUSEbzmGVx4m6XOJyXnTjU2NoKPY2pXo9kN6tXLA9vadF1WEVBavPR5sWMEYXM2da8emnBvh8QG2tXuZon3eeBx98YIjLYQ3m/PVLYHDJb5qCpRxnt8NcXe3/vbzcF7NgtVq5hH3/YqWjg9Y6joXgm2fG6CJspMrGjRvx3Xff+X///PPP0dbWFrJfb28v1qxZg8mTJyd+hAzGCEcplpEQGhIQ6rDKRWc0Za20uAtmM9DZqenl/AwN0WXsYMQY1XgcVunF0eWiYj3WYv9K444n6WogrwSZPaHZG87CEplglTroubm0nebAAO06xRhdmM0EJ53kxWef6WG30+Q3kcpKNx56yIzf/CZx7aYyreG7XIlIu2GVl/vw4YexleCyWkNL7R1q2tv5mAWrOAcaDCTk5p8x8gkrWNevX4//+Z//AUArArz33nt47733FPctKyvDH/7wh8SPkMEYZRiNBC4XdQCdTrnA9Hg4WbJBtA5rJHeBOqzRuS+REknUYlgjOaxKdVjjybAOxm6PfXlQpwM2XrYMs1bfKQsLsPNp6Ll3Cay16g5rby+n2uWKMfKZN8+FW25Jw5w58i/mxIkEJSW+hDqs9oISpHVGTnmXdsOqqPChsXHkOqydnbF3u7Pb6Y1iejo1BHJyEjw4xrAS9q96wYIF2Lp1K3766ScQQvDEE09g69atsn8///wz9uzZg61bt2L69OmHatwMxoiALg3LH6MFwmn70mCHFZDvH43D6nRqc1ij7TceLiSAEPp6UidT6rCGG49Sp6tEOiJ2u7IzrAW9Hth50lzYly+Ha2wpCMfBPa4UfzrmJeiuq5KVtbJaeVkMa08Pr1oZgjHymTbNC6NROcGtstKT0M993x1LYeflf8TBr06EQLcrACgqIjhwgF7aN2/WobdX+/GowxrraBNDZycXMXFRDfHmOT2ddbsajYR1WLOzs5F9sILx999/j+LiYmTGE1HOYBxm9PcjxGkTBHKwfFVk8RhN4wDtDmtikq5oDCoVtMExrF6veqcn+fMDYwkOh4iXeJKuCgoIGht5uH9bhRWOefjxRx0WLXJh8x9MWCjYZZUMBgbgFynjxvmwaZNO1t2MMbrgOOCjjwYxfnzo5/vQQ46E3nQV//Yq9GQSjH2Ollbbry9F/vXnQ//5FzQMoLQEjiVLZE0FOE5c5QAefdSMu+924vzztVWPsFo5zXWfk4VS/WOtOBx0rsrIIIplAxkjG83rBhaLhYlVBiNKentDE67MZjqx0hjO4GYcCNk3mhjWyHVYE+ew0iYIgYuEiF5PBavVyitWR5DuF1yH1WgMxJ/Fy9BQ7HVYy8sDmdaNjTwaGnQHSxaFP46Y8BJveSNGalNaShTrnObkIKHtcTkOyLitCrZt29BvteLCyQ1wPPMMmr+uQ+VMF2zbtoV0wAJoibOdO3ls3KiLWLVCSm8vpzkEKRnEm6wo3jwzh3V0ElV54D179uCVV17B1q1b0dfXB19QOxuO42RJWgzG4Y5SSSuzmcY5ainiT0MCtB0rWDgqEYvDqra0LiZNBTusohCl567e8kqnkyddOZ20UYIgUFdXbE8ZK3Z77DFs0kzrhgYeTU18iFsuNgiQvj8TJviwdy/PHFZGUqDd4YD2dg5FRerfrYoKH95804j8/Mhl1qRQhzURI40Nq5VDXh5BTw9ian4gzoHp6Yg5rICRumj+c/jhhx8wY8YMrFmzBoIgYOfOncjPz4cgCNi1axd4nsexxx6bzLEyGCMOJZdREIDubl5TyaXokq6S47AODiovrRsMgQYBUqEsJl1Jk5GUCI5hdbupYE1LU28eYKipQeaUKf4i6tL6k8HEExKQnU07lAHAvn08srMJurt5v2sqbRAgPY5Y57a/H3HV42QwlCgoIOjq4tDezskqFARTUeHF6tVGXHGFW5NgFb9Xa94zY5utPOz3Kpm0t3MoKCDIyNDe4U8Kc1hHN5oF62OPPYYxY8Zg8+bNeO211wAADzzwAP75z3/io48+QltbG2644YakDZTBGIlQ0SZ3Qsxmgu5uTtPSYXRlrZLlsIYPCfB45MlSgZAA5fqzgf2CY1hpSIAgKAtWQ01NSOep4KLp8nHHnnQF0FjjoSFauWHSJC/q6gKCVawGAIQK4/x8gr17eRYSwEg4BQUE7e0cOjr4sA5rebkPhAAXXOCWJQgqIf1e8SAo8e4L+71KJh0dHIqKCLKzSUylrUJjWBmjiagc1htvvBH5+fngD/r05GAhxhkzZuDaa6/FY489lpxRMhgjFCWXURCoS6LFYY3GEU2Ww6rW4jS4jqqImHQVSbCGlrUSHVYg7cM1fifVPX4qDDU1MFdXh3Se4ux2OO+thhJqzrBWJk70YfNmHcaM8aGiwoetW3X+Zf6cnMBSq90uj1ssL/fK9mUwEkVRkQ8dHXxEh/WYY3yYP9+FwsLwIQELFggwPqr8vZI2IwCAW26JsahxFLS38ygs9MUsWAMOKy1rxRhdaBashBDk5eUBANIO2hZWq9W//aijjsL27dsTPDwGY2SjJNrMZtqzOyMjuAYrdR2lUBdzeB1WteSl4CV9ETGGNVJIgF4PjP/nO35huujpo3DUD6txTtvbOOKPd/md1DGD1PHhmpXrUWb3KxdXj9ShKxIVFT7U1hpQXu7DxIlUsIquqXTJMdhhFcUtc1gZiaawMOCwhhOsBQUEjz3mkK0EKLFxow66NuXvj7QZAQB89ZVe8fueSBLlsKanEwwMMId1tKFZsJaVlaGxsREAYDKZUFZWhg0bNvi3f//998hhVXoZDBnqDiuPtDSaESt2jBIdRinBVQPCkSyHVa2slcFAFEvg8DwtTRNJsJ60czVOe22RX5jm9DXjjDcW4Zrv7oXOGer4yDoqSOg0l6L3xdDY1nhDAqhg1aO8nDqsXV283zWVLjkGO9Dl5XRfJlgZiUZ0WDs6OBQWqocEiEhXApTo6ODhKChR3CZtRkAIncuS7VpSIU4d1v7+WAQri2EdzWgWrOeccw4++ugj/+//+Z//iTfffBNz5sxBVVUVVq1ahSuvvDIpg2QwRipKos1shj8kQFpnVUmwAoEWqJGghfrD72M0ai+TJeL1ctAr1BNRCwkQRfbAABe2Pen565dAH9QnXe+yI93RrTYQDHFBRdQFAS0nzMa4paGxrafVr44rJKCiwoe6Oh0qKqhgBQJ1ZaVLjj4fJ8tmDuzLBCsjsRQWEnR0cH4nMhLhyuINDtLv6KYrHgUJmjh8ZnkzgsFButKTbBFIqx8kIoaVhQSMRjQL1nvvvRcvvfQSXAdTlu+77z7cf//9aGpqQktLC+655x48/PDDSRsogzESUarDKghi0hWB0UgnWYC2JjUa46lBGL4VKhCdYxsJo1FZsGo9Xnaftj7pIp5xpVh+3EtoM5TBBw7eklLYly/HcU2fweAOdWSv/+WRuB1W8f+iIoK0NCILCRCXHIPPceJEubhlMBKFKFi7u3nk5WmbK9RueDs76R/u95Ou8Xd084FDu7kMbcuWy+q7ii5tsgVrvCEB4s0jc1hHJ5rrsObn5yM/P9//O8/zeOihh/DQQw8lZWAMxmggnMOakQFJLVbir0MajFaRGakVqhSnk5ZrslgiLyuqFfI2GAiGhjjFMdPnhX/d/pwSZPeGxqXahTwYPHaZCCWCgNaFS/H9D1ejdeZcDA4Czz5Llb5w222Kr1/obEZXHDGsubkEOTk+TJjgA8dRIaoUwxpMejpQXOwLiVFmMOKlsNCH9nYehERXo5QQ6l4ajfAL3fZ2HhMm+NDezsF9axW+q7gaf/ubAT09PJbNtiNT0iUqIFgTdy5NTTRxTHpTKbY5zs4maG5WPsGmJg4//KBHUZEPZ53lVdwnPZ34y9IxRg+a/uTtdjvGjRuHZ599NtnjYTBGFYODoeWrzGYiCwkQHVax01Mwer22zk8OR+TWrCKff67H00/H1wdVr6e1SpXaqdKxhBds/3fxMrgN8qVIr0nA11VP4/OrXoCvlDo+e1EG+/LlaD1nDgQBuOMOJxYvDhSnlcbaSTlgKFUV01rgOOAvfxnyh1n88Y92FBSIMazh3aaXXx6K69gMhhKZmVQ86nTab4YyMwlsNmDFCiM+/jjgUbW3czjuOC86OqgMEG+ulUrpiYI1kYlMzz1nwvffh8alcxzCOqx/+YsRX32lx/33q8c/lZQEGn8wRg+aPlFBEJCRkcFaszIYMRDsNAoC0NtLGweYzYHEJTWHVez8FAktSVcitbUGTQ0JCAEIUSnib6CCVcnV7esLX9IKAPaeORefXEGFKeE4dKaVYc/vn0PrzLnYMvlq2LZtw+zznJg5oRGDl1X5qxWUlBCUlwecYceSJfAY5RcvIgh4rvjxuEMgzjkn4ODMmOH1531Rh5V244n0PAYjUYid1cQbJy2IlQIaG3n09nL+JgHXXp+Jt74+ApO3rAYgFayhzUp6ezkYDIldZrdaOQwNKb9eVpa6YLXbOcyd65LVfhYRV4Oys2mOZnc3c1lHE5pvQS677DJ89NFH/tqrDAYjPGpfFVFUZmTIHVa1pKtwnZ+kaClrJbJ+vV5T8pXLRUthKWEw0GU3JVfX60XYCgEAHeuWydf4+6SvPLYaE/68DPNvzsCip4+CoaYGXV0cSkp8GBpSrwfrrqpC31PLsd9YBsJx8JXS2NZPcudFPL9YEUMC6HvO5kTGoaOggIQtaRVMbi6tFNDQwKP82zWB5hsgyO1vxt11C2CoqfGX4DMaQ6t/WK0cxo0jqgIzFqxWTjaveTzwO8fhHFYtyaUAcO65HqxfH1X3eUaKo1mwXnXVVeju7sYll1yCd999F5s3b0ZdXV3IPwaDQaFllUIvLOJkm54ujWGlSVdK4lCrYKVJW5HH5fXSJTMt5a3URCJAQwL6+5WX/vX6yILVbCYQ65Ubampwx48LYToQuJAKixdjdvfbyMig5x+u1ar+hirMOW0Pdv/SC9u2bbKEkWQgCtZ4a70yGNFSWEg0lbQSER3WhgYeF32zJKRJgECGYK6uRm+vusNqtfIoKfH52xEnAuqwBn4fGAi0Mw4nWMXkUo6TmwI+n3w167zzPPjySyZYRxOaP80LLrjA//O3336rul9PT098I2IwRglqnZ5EgRccw+pyQXGZS2tIAKAtQctsJrjwQjc+/1zhYEGoNQ0ApCEBodt0Oi0Oa6CJgbm6GrxXfpKc3Y7FBx7GolPmwm6n4jBcd7DKSuqozJ1LA34TWREhGLFsjlqNWgYjWRQW+qJ2WBsbeWRkAHkqzTe4lpawIQFWK13pSGxIAC+7Ee/r4/xJjVlZgM2m/DwxudRopLH94k16cAz/CSd48fPPOvh80SWoMVIXzYL1mWeeAZfMKwCDEQevvmrEr3/tRnFx6rhdSiWtAKnDShLqsGqloIDg4os9+Pvf1QXrN9/o4HJxKCvzhRWs3d3KcbN6PdHgsMLvsAZ31REpdjcjLY0Kw0jicPZsN665Jh1/+Qu9giXT+RTLWoUT9AxGMigr86G0NBqH1Ycff9Tj5JM96OgoRbFzX8g+3nEl6OzkkJdHYDIphwSUliZWsPb2cpCavTYb569dzPO0RJUSojAVb+QDglU+F/E8cMwxXtTV8Zg6Vfv7xUhdNAvWm266KZnjYDDi4ptv9JgyxYvi4tRJdhkaUnYEAzGs0OywJrKczKpVQyAk1EWRsnmzDnY7hzFjfKoi0WAgGBjgFZcn9XpETLoymwMOKykpUWy92p1WirQ0GjsXLiQAAI491ofvv7f5E6GU3stEEQgJYA4r49Byzz0asiUl5OYS/PCDDldd5cb//vg4/rvzdllYgEOXhuZbl2L7Gh2OPNIHgyG0G57VyuGkkwiamxMjWL1e6qhKBbDNxsm6w6n5Y2JyqSDQeUGcZ2iogHzfWbM8WLdOj6lTo3vPGKlJTEZ5V1cXduzYgSGt65QMRpKx2xG2BeFwoDSBAlTgXIOVKJtxHP7wRBouXTwZhpoaOJ1QLBGVaIeV46j7oJb9D9AajR0ddBleTSSKIQFK56gtJCAg1h1LlsDOy6v8uw0CPpuxDIJA30strVbNZrpPWlqyBasYEsAcVkZqk5NDsGMHj4oKLz7NnQf78uXwltCScb7SUqyufBHvmebh6KNpFQyTicDtDnVYaQxrYuahvj4Oer18Xuvv50LaGSslrorJpbO7V6Gi8jh/O2bhg5qQuaqy0oPa2iROBIxDSlSCde3atTjjjDNw5JFH4swzz8SmTZsAAN3d3TjnnHPwySefJGWQDEYkhoa4lBOswUtUIjmfrMGruA26FppglNFNE4xKv1qjWIc10YJVCx0dHNrb+bAikQpW5Sx5LYJV6rC6q6pQPf5leEtoiav+3FI8UvxntMyY63dSUkkc6nR0yZIlXTFSnZwcAp+P85eCc1dVoeWbbTh3hgu2bdvQMmMO3n7biMpKDwAoOqwDAxyKihIXEmC1chg7loSEBGRnB75LGRlEMcnL5eKQ/lENFm45mKR5sB1z2WN3oXL/27J9i4oIhobU42EZIwvNgnXdunW4+uqrYTAY8Nvf/lZW3krsgrVq1aqkDJLBiMRIcliz/liNdIQmGM187SYs/n+0nJMULUlXbjeNG40HsT5jVm4unv//LDhxx2oMDoZLulIva6WlSoDUYQWAT3LnYaCOlrja91Ud/qf5On9LVDHpKtXEIUu6YqQ64vewvNx3sCkAXUEpKqICtrCQ4OefdZg1iwpWk0k5XCgzM3GhSVYrh/HjfbIyWcEhAeEqBQiPVcMUlKSpc9ox9+clIfuefbYHX3/NqgWMBjQL1j/+8Y84+eSTsX79eixYsCBk+7Rp07B169aEDo7B0IrdnliH1enUnpmvhprDyrcqJxhxALJ7qdsqFa2iwyhFKi4zp0wBt6pGc5crJQw1NYH6jISg2LkP/9V0B8auX6MqEsVOV0oOq5akK5OJClHxXLb8bEDmlCkw1NSgtJTgqKO8KCjwIS0tdTPyI8XVMhjDTW4uLYOVmRkocdXezvkrDRQWEhxzjBdjx9LfjUaCim/fkc0vF/asCtuOOFoCgjXwmM0WKGsFhBesakma+YOhcfBiHCtj5KNZsG7btg1XXXUVeJ5XrBZQXFyMzs7OhA6OwdDK4CCH3t7ECdaPPzbg+efja12qVshfrZWoCGe3w1xd7f89PR2ypbNgcck3NyP3gcW4qDf2FQ5zdbVifcaZK27Ffz2S7heSUsLFsM6f75It7ykhCMDM1rf958KDnoso2B95xIEpU3wQhMh1WIcLmlg33KNgMNTJyAAeeoguZeTk0CYCbW08xo6lDuvxx3vx8MOBpY7JW97B+e/eKZtflrTegbGP/xZvbpjkF7HB80E0iI0IgmNYxSoBQPhuV2pzaG9W6OOnnebFli2hLWAZIw/NgtVoNMIdpqF5a2sra93KGDYSHRLQ28thz574ivepOayOJUtAIliFUgch2GFVEpe8w44Htt8U88VEzbHgfV5wEiGZ9+mn/m0GA0F/v/I5XnedO2LtQ4MBWNjycMi5iIL9kks8yM0lSEvTnnR1KDGZCPr6WKcrRmrDccCNN9JrtyhYGxt5f0xrXh7BRRd5/Puf/vFSGN1BN6++IaS/tQJjXfv8IjZ4JSgS0lWhOb87GrMOrJLNa8FJV2oOK8cROJYsgcsgn0NdBgFbqpaF7G8yISSJjDEy0XxFPvXUU/Hxxx8rbhscHMTKlStx5plnJmxgDEY0GI2JFaz9/XRSjwc1h9VdVQX78uXwlZZCTepIHYTgpCs1cakj3qgvJmIoeiTXF6BCcvyLL/p/1+upsx3rMj3H0TqritsUBHsqJV0B9HPp6uKZYGWMGMSQgMZGHhUVyrVJ07tVQpaCUvaDV4LCEbwqlN3bjIs/uhMzWgJJUtHEsLqrqvDFVS+gP7fU3475jekvAfMCHe6kAvnTHRVxOcKM1EDzFfmBBx7ATz/9hLlz52LdunUAgB07dmDlypU499xz0dPTg/vvvz9pA2Uw1CAk/PJRLNhsQENDfIJVrBeohLuqCrZt2/DTva/DqZfbhkQQ4FgSSB4ITrrSKi4jXUwMBtopBtDm+gKAsb1d8nz6v9o5auGAoVTxcSXBHq7E1nCQnk6rKaSS68tghCM3N+CwTpyoLFiHxkSeX0TUbp6DUVoV0rvseKzxev+KkBaHlSaX0p/3njkXf37wF/RbrbBt24a3uf/ElJ9XU5GakwPhttv8Anmce1/UjjAj9YjKYV29ejV27tyJW2+9FQDw4IMPYtGiRXA4HFi9ejWOPfbYpA2UwVDD7aaF3D2eRApW6uj19cX+GmoOq5SeC+fgzTNfPOi2chgcUwr78uVwVwWcguCQAMeSJSAaus5FupiYTIHyNTLXl+Pg5ZRjvlxFRf6fRcEaTyLUc8WPhwjlYMEu7XSVSuIwPZ2gqyu1XF8GIxyiYKXtTZX32Xn9o3AF30QjuljSYNTmIg7wrwidtnu1LIY1O5uGHEmRVl4R5wWR0xvexpiHDrq4iM8RZqQmUVlI5557LrZs2YIvvvgCL730El544QV88skn2LJlC2bMmJGsMTJShL//XY/e3uEeRSjJyB7v7+dw3HHeuMICwjmsIiYTwYbx18C2bRsWL7Jj3YrtMrEKhCZduauqlCtqBxHpYhLcglF0fZ/70wA+u+ZVuI2hQrJ14UL/72IZLaV2slr5NC+0kLmaYHe5uKQ2A4iWjAyCzk4+pUQ0gxGO3FyCpiZeJgyD6Tx/Dt6a8aL/5rU/txQ/nn5LyI2lTxDwzUWPqr7Ozp08Nm2iN77u4siJprc0PByxSgAV2nTsYgc8gN54390eGg8fchyNjjAjNYn6aszzPE499VRcffXVmDdvHqZPnw6djmXgHQ7U1BjR1BTfMnkyELPHeZ7Am6DOrP39HI4/3ovGxtj/tsO5GCJmc8DlVOt0pVTWipQqL6X7twe5lEoYjYE6qNJ4r+seORrZWQRbFjyPJkzwx4jZly9Hz4UXyp5Pxxf+HCPhuqoKnZu24YxptJB5sGAXk64A9XaNw0F6OtDVlVphCgxGOHJzCbZs0anGrwL0e/1/E66GbRutifzft+3CL3c9C/vy5dhvLPPPB1sWPI//+vf1qq/zr3/p8fnndP1+85WPwqkLf2dX7G6GVEooCVapwypWDwGAvXt5FLmU4+GlaHWEGalJVOrD5/PhnXfewS233ILZs2dj9uzZuOWWW/DOO+/AmyilwEhZbDYkrDVfIhELyistIcWKzUYFazxxrHRyjeyw+rs9uQMiUIpSpyulmNqYe8QAACAASURBVFMCDgRQdCmVj027xgQnROT2N2P6G4uQlUVQjib8vKVXUUgmIobVaCRwuWihcqVzBwKCPZXEKkBDAjo7mWBljBxyc2mTgPCCVb7yIiZouauqcN3Zu9FQT+eD/5twdVgDo7eXQ0cH3b75yKvx5dznYS9UTzQNjmdXc1jF+UZ6I9vYyKNPoaSVFI8p8k08I7XRfDXu6urCrFmzsGDBAnz22Wfo6+tDb28vPvvsMyxYsACVlZXo6upK5lgZw0x/P5ewwtGJRIxtFOOzEsHgYPwhAdLlKzXM5kBXGaeTU2zNqtTpKjjm1JZXio/nvI7+XmVxqYTJRLveKCVE6Jx2HPH6Mv/xlRBDAuJxWM1metFxuZTPHQgs/WmIgjikiHHTrA4rY6SQk0PFaHm5usFkNMo7XUkTtNLT4W+X2tCgw4EDHNRW4a1W2qAAoJ21rBfOgX3HNvxu3F9h50ITTZ8rfjxkrJEcVvF61NDAY/OVy0Jv4jl6E9+bXYqvr3tB07zISF2iqhKwbds2PPXUU2hoaMB3332HjRs3oqGhAU8++SS2b9+OBx54IJljZQwzNltqClbRYc3NJTAGdYCKNSuUENrKMB6H1eFQbs0qRYvDSi8goe+7GHPab7XitYd/QdMZc6Man9FIwxDU4rr0+1vA80RVdCfCYRUEev7hHFapk5JKZGTQ/5nDyhgp5OQE2rSqERzbLg1tkna7amjgcdJJXuzdqzxHWq0cOjrovh0dHIqKCHQ6oOH0uVgy9mX4Smncumd8KWz/bznWFl4je35mJtDfL39NqcMqje1vaODhnSu/ifeVlsL+yivo7+3FW4/txA9HXa3tTWKkLJqvxl988QVuu+023HzzzTBKrixGoxG33HILbr75ZnzxxRdJGSQjNbDZOP/ddbKIxUUTk64qD7yNo55ZJOvQEmspE44DsrPpOceKlqQreQyrssuoZSlc6jxoRbwwqcV1kZISFBQQVQc1EVUCzGYq7KnDqn6coSEOBkNqCcP09PgdZgbjUKLX0xKA4QSrwRCYk3p7IUvQysgICNbmZg5nn+2R3dRL52+rlUNnJw9DTQ3+sPJInDc7C5lTpuD2zJX4tpwmml4024ndX9ahe/YcWUkrANDpAK83OCQg8H2TxrBKwxbEm3jpSlMiV98Yw4dmwWowGFBeXq66/YgjjoAhlVJ4GQnnUIQELFokYOfO6FxNMenq4v9bAr1LuWtSNBASmHgNBiJbHosGLWWtqMtJ39NwLmPkY0UfSymWtVKMhz2YtDVvnks1M99gIDAaScSOVuEwm+lFx+UKX23Abk89YZieTmA2x3f+DMah5sYbXcjKUt9OO0PRn5ua5A0GRIeVEComLRafLGzq6adNWLuWJlpZrRyucq2EsHgxCu2BDlnnvXMn7h/3FoBAfH5/P8KOSURqAkiTUTs7eRQWqs8fTLCODjRPtZdeeik++OADxeQqj8eD999/H5dffnlCB8dIHbxeGteZbMH6ww86fPGFPqrn+JOu+lRq/UVZyoQKTTr5ZWSExo9qJZxr6B+b5O2koi22Y8XjsAbHw3ZnlPmTtpYudao+X6+P/pjBCAIVzU4nwpascjo5v6OZKmRkEFaDlTHiqK52hN1uMBD/TXRDg07mxooxrO3tHIqLfaiokAvWv//d4G9pPTjI4YG+0FJTvMOOX3+3FAD9/g8Ohna5kiJ1baXhCcGhQuFWophgHR1oFqzXXHMN+vr68Ktf/QorV67Et99+i++++w5/+9vfMHv2bNhsNlx99dWoq6uT/QvHgQMHcMcdd+CII45AUVERpk2bhm+++ca/nRCCJ554AkcffTSKi4tx8cUXY8eOHbGfLSNmbDb6/+Bg8o7h89EqBLW10Tn1YtLVYL760nY02GycfxksPZ3EVRkhmsz2cIlHkZDGdmlFjGEFAvGwdy20459/Ca0Fq4TBEH/8puiwut1cWIfVZFKPpR0u0tNTq5EBg5EIpA5rQwOPioqASSU6rA0NPMrLqWAVQwI6OjjU1/P+uFUAKHSGb72cni46rMqCNT2dyAwDaeUVgwFwuzlN1VjElrSMkY1mK+uCCy7w/7xlyxbZNnLwFki6j0hPT4/i6/X29mL27Nk4/fTTsWbNGuTn52Pv3r0oKCjw7/O///u/eOGFF/DCCy/AYrHgySefxBVXXIFNmzYhU1phmJF0+vs5FBT4klrWav9+Dsce60VnJwfvWzXIebIaXEsLSEkJHEuWqIoo0WGtn/8ojvnTXTB5AjOclnqkwUgnz0DMVvLFUriQAL2etlFVcyG1hB8EI+10JfKvf+nw8MPhHRgRgyERDitNuiKEhHVYBSE1QwJSTUQzGPEivZFtbORx/vlu/zZxPhQFa34+bZ4BAOvW6XHRRW60twd8sN7MUuTZ9oUcQzQRBIGKULudU2xmIJa2EldXlEKfOjq4sOEAABOsowXNgvWZZ54Bl8BCiMuXL0dxcTH+/Oc/+x+bOHGi/2dCCF566SXcc889uOyyywAAL730EiwWC959913Mnz8/YWNhRMZm41BcHFrAPpE0NPCY41mFOf/+LdLv6vE3A+QOJk8BkIlWQ00NzNXVuK+5BYP5JWhbtBSvnPIi7mx9RJPQVYMuT9GfpVmxySacIBUndqXthpoaPPH2Y8h/vTmqczYaA0t/ANDSwiE3Vz3JKuS4hvgFm1jWiufDh0OkpaXe8jsVrMM9CgYjsfA8QEigEkBwSEBrK7B/P49TT/WC4+g84HYDtbV6XHONG3/+s/FgzDnBP3/1KC75aBGMKiaCGIc6MKDssIqCddw4us1uB/Lz5ft0dPAoKlJPIqPHQUgta8bIQ7NgvemmmxJ64H/84x+YNWsW5s+fj6+//hrFxcW4/vrrceutt4LjOOzduxft7e2orKz0P0cQBJxxxhnYuHEjE6yHGJuNw7hxvqSEBHz9tQ5nneWFfk0NrvvqThjcoTWMxOQpUYiJxe7F+KiM7mZM+p+78L7lZdi2bYtrPDQBIFA65VBWRlC7JxSTE7KyCDo6aCxxebnP/z6kHXwf1MS9EsEO67p1esya5dE8buqwxiciRYdVr0fYcAhBSL3l94wMpJyIZjASCQ2PCvyenk6wYYMBViuHuXOp81pW5sNTT5lQV6fDiy/a8Yc/mGC1csjJIeg8fy6WN+gwv/4R5A2G3lAHkq7CO6wi1GGVi9P2dg4FBRpaVbOv6ognuuyWBNLU1ITXX38dCxcuxD333INt27b567jedtttaG9vBwBZiID4+/79+1Vft76+PnmDToHjDRc7d2ZDEHLQ0WFM+Dnfe+8xWLZsL857/xFFsSrCtbT4j6175BHFYP7rdz6M+vpj4hrPrl05cLkE1Nfvh9M5DvX1gxg7ti/q13E6J2t6rwg5GnV1u+F0Hq26v89Xge3bW9Hf78Q77xRi5840LF3ahCkK7wNnt0P3yCPYfsIJYY/b2zsGbjeH+vpOAMBHH1Xg9tvbUF8fPiRAHCMhwP33C6ivj71Iqs1WiKYm78FqDAbU17cr7kfIJNjtQ6ivb4v5WImGEGDx4vjOP1oOl/kmlTkcPgOnczJ27NgNr1c+JxUV6TBjRjb0egKfz4r6emDuXDN27UrD/fc70dQ0iKGhydi6tRk6XRE8nh78qWMOGq+vxPz5BwIHOPiaAwMFaGz0obXVhOOOG0R9vXye9XrHYceOQeTn08dbW3uQlxfYz+mcjG3busDzBPX14RsXOZ2TsWtXfcp1zBspHIq/e4vFEnZ7VIK1t7cX7733HhobG9Hb2+uPXRXhOA7PP/+8ptfy+Xw48cQTsXQpzRY8/vjj0dDQgNdeew233Xab7DWlEELChiZEOuFEUl9ff0iPN5z8/LMBRx3Fo71dn4RzNmH37grMGQwvRkhJCSwWC+rr62FsVxY2xe4WpMc5vk2bDJg4kYPFkoGyMiNycnJgsRRG9RpuN5CRYdL0XpWVmZCVZYHJpL5/QYGAwsKJsFh8sNnM2LzZgEmTDKrvg7G9PeKxy8oM6OriYLHkwOMBDhzIwPnn68JO6MF/80ceGfH0Io5BTJw49lgCi0W5tk1BgYCSEhMsltRqKxXv+UfD4TTfpCqHy2dgMpmRk2NBWZkx5HxPPFH8aQwAwGIBLrwwsF0QTMjImICyMgNOPNGE1lYzJk/Og8USmncyYYIBQ0Mc+vp0OOuszJB5trzciPR0Ov/W19cjLS0fFRVZ/v0yM01wu4twyileWCy5Yc8pL8+EceMs/oYfDO2kyt+9ZsG6du1azJ8/H4MH14RNCgFn0QjWoqIiHHXUUbLHjjzySLQczB4sKioCAHR0dKBEkuXd1dUV4royko/NBhQVkaR0HPJ6afzTTYZSjHOHBugDoclTpKQEXHNoBuoBQynilTTyKgGxVUaIpsxUYaHP33NbDXHpDAjURvz3v3lMV3kftFRGoGWtDvb63qzDKad4D7n7YDbTEIyODh7HHqsejpCKSVcMxmimvT1yMpMS+fkEDQ08cnMJioro88X/g0lLA7q76Zw2YUJoHGpWljwkwOmUzwOCQJ978cWRQ5nE0lYZGSw2YKSiuazV73//e+Tl5eEf//gH2tvbceDAgZB/4Zbqgzn99NOxe/du2WO7d+9GaWkpAGDChAkoKirC+vXr/dsdDge+/fZbTJs2TfNxGImhv59DdjbxB+MnkowM+vp/Knw8tIA9AF9enr8uqIj7V78CCXbfFfpRx4K0JmCsZa2iKeRfWEj8PbfVEIRAPdj9+3nMm+dCba0ejiVL4NCF9uXWUhlBmg1cW6tHZaU7/BOSgNlMY1jpxVE9cSItjWXkMxiHEi3JTEoUFhLs3KlDbi5BXh6BTkdUv9uCQJNanU5OMekyOIY1uHugIBDs3cuHnTtEWC3WkY9mwdrW1oZFixbhjDPOkLVmjZWFCxdi06ZNePrpp9HQ0IAPP/wQr7zyCm655RYA1K1dsGAB/vSnP+Hjjz/G9u3bsXDhQqSnp+Oqq66K+/iM6FCrk5cozjrLix+PvtpfwN4HDq6xpbC/+ipsDQ0ysZr36acwrloFThKSQjgOrnnz8Fn+NUovHxXSBIBYqwTY7dqbABQVEU0O69AQB6+XJmadd54HtbUG2K+owpLil+EtkfTPDhL3akiTrjZs0GPmTO0JV4lCEGhJLrHXuBppadTtZjAYh4ZYHdaiIh9++YU6rDxPBay6w0rQ1sYjL095e05OcNKV3GFNSyNoagrf5UqElbYa+WgOCZg6dSqsVmvCDnzSSSdh5cqVqK6uxlNPPYWSkhI89NBDfsEKAHfffTfsdjvuv/9+9Pb24uSTT8b777/ParAOA+E6kSSCSy91IytLD3dVFdxVVbjnHjOuv96Nk04K7aw2/sUXQxONCIHhiy9A8uKfkGy2QJvAjAztIQF33y3gf/+Xjis6h9WHf/9bF3afzEyCnh4OLS0cxo/3obiYwGQimDkzA+ddNQcDyy7VNkgJYlkrsQpCTk7ULxE3YuOAnh71ixYAlJb6YnJ7GAxGbLS387BYQuffSBQUEPzyiw65ubSn9emnezBmjHpIwL//zeP445WPk5Mjd0WDHda0NMDjUXZng2EO68hHs2Ctrq7GDTfcgMrKSpx66qkJOfjs2bMxe/Zs1e0cx+HBBx/Egw8+mJDjMWJH6rASEl0Hp3CIJukpp3hxyimBSSvcMrlaohHX0gLkxT++4JAALQ6rywW8+67BL1ijiWEtKiKoreXCjnn6dC9WrDBi/Hifv7d3TU2MPWMPYjLRcXd10Rq7w4HZTF0TQmj9RzVuvdV16AbFYBzmGI0Era0czjwzFoeVoLWVR04Ofe4bb6gnPggCwY4dOlxxhXI40oQJPuzdG5gYlBxWrTeyzGEd+WgWrNOmTcMTTzyBCy+8EEcccQTGjx8PnU7uCnEchzVr1iR8kIzhR0xEosk/iauJ6XJBsSVnURFBZ6fy5OIqKoLpwIGQx0lJCcxmAqczvg5MUnFe9s07+MPKamS9Fr4of3Mzj8FBDj4fFV7RtEotLPShpYVX7XIFACee6MVPP+lwyik6v2CNF9Fh7e2lDQOGA7OZwGbjoNOx+FQGI1UwGICWFj5smI4aYjypljklLY3OldLmBFJyciATmaExrNActpCbS7B3LxOsIxnNMawfffQRbrvtNni9XrS3t2P37t345ZdfQv4xRieiYE105yenE7i4721kTpmCrNxcZE6ZAkNNDQoLfbIWf1JaFy4MTc46mGgkCCTujibiuRpqamB58i4UDO0DRwj4g0X5DTU1Ic9pbKRjdRwsYRpNq9TCQhJRsOp0wOTJXvzjH3pZb+94MJvp+2+18sjNHZ7ldkGgF0Ythb8ZDMahwWQiB7+X0c8LosjVIljFsKlwN+EmU6A6jcMhNyMEQbvDSsMLNEseRgqi+dN79NFHYbFYsGnTJjQ1NeHnn38O+bd169ZkjpUxjNhsYmef2Mo8qWGoqcHv6u8A39wsE4XH172Djg5l4dlz4YXof3o59nFlIODQZijzJxolYnxDQxwEATBXV4N3hBblN1dXhzynoUEUrHTMwU5AOASBurrhOj0BQGUlTbSaODEx4lIMCbBah9dh3bdPW9IEg8E4NBiNQHc3F1PN0qIiHziOKHauCkbsFBduTisvD4QFeL20K55IejqJwmH1sRjWEY5mwdre3o6bbroJkyZNSuZ4GCmKOFEEl3mKVI4pEnlPV8Psk8dicnY7jvrro4oOq7ikc2DWHMw5bQ9mnOXCRcc0hLT6ixeOOxgTq7RN4fGGBh5ZWVIngIuqbqggkLAOKwDMmuWB0UhQUpIYcSeGBAynYBUEoK0tfEkrBoNxaDEa6VwaSy5AdjZdNdKFzyMFQA2Q4mJf2AogEyf6/IZAMIKAKB1WJlhHMpoF64knnugv6s84fMnICIQE+HzAtGkZcMWRD2M4oPw3ZTjQouiwXnxxBgihruCECT5ZyRNAXq80XtSK7ys93tjI45hjvHA6RYcVmh1WgE7wkQRrcTHBX/86JHMY4kEsayX2/R4OzGYCny+28jkMBiM5GI3qpagiwXHA009r6zBjMABPPRV+34oKnz/kKphzzvHgggu0leNjVQJGPpoF65NPPol3330XH3/8cTLHw0hRxGx+aQxrTw+H3l4eGzdquJVWwVmoLgqHhuSTi8NB4x0HB3V+V9BsJjAY5IWk43FYpd2GHUuWqMbKBrN/P4+JE31BDqv2Cb+oyKeYfBaM1slZC1KHdbgEq/j2xnpxZDAYicdo1J7MpMSvf619noq0bzjBarH4cPTR2hzWrCz4S/gxRiaavZoFCxYAAG688UZkZ2dj3LhxilUCNmzYkNgRMoYdtxvQ60Nblba3cxgzxod16/Q4++zYEoH23PQoJj15F0zegC3qF4XPyfdta6OTVleXHi4XFazl5T7s3x+YzOINCRgaCsRViWEGvQsfw1iPepUAsZh/WhqRxLAC+fnaj1tYGFtHrXhIhRhWnqfCmYUEMBipg9Gofak92VRUqIcERAPHISmdGhmHDs1/BUajEWPHjsXJJ5+MSZMmIS0tDSaTSfYvER2wGKnHwIBy56eODh6XX+7Ghg2xr1HvPWsOVp7zInyloZ2aLh9ahfRjA9UDfH+j2fnd3QZ/Kabycp9fYAI0JmrMF++EVB3QSnCDBHdVFS46pgH9Vits27YplrRqbeVQUuKD2Yw4HFaiyWFNJEYj4HINbwwrQLN+mcPKYKQO9CYyNb6TY8YQdHbystUvxuGJZqXx5ZdfJnMcjBSmrw8Qm4tJY1jb22n9vF9+0aG9PXxrTTWcTg51U+fC9t7lsscNNTV4sPFu6A86r1xzM6Y+vwi3ZxrR3T0dhNC4x+xsgl27Avddp+x6G9M+XgTeZfc/T1i8GAA0tSsVS1pJ4Tjir6+qRGMjj4oKH4xGucMaTS1YWsYr9tCKWBATKoazDitAwziYw8pgpA6p5LByHF3hGxzko8oLUIKGQWlvm81ILVhRshHMK68YYVeJVycEePbZxHwrbTYOGRmhIQFi//fKSg/WrQu99+ns5PDOO4awr+1wKE8e5upqmL3y7CmDy45l7v+SOaxHHunzt1EFgFm1S2FwaStFpYS0aYCIIASc095e4K235OfU2MijvFzusDqd2staAUBJic//Hh9KOI7egCSqEUQsjB/vA+u2zGCkDhkZBOPGpY6lWV7uw/33T4p7jhS7Xf34ow7r16v7dR98YEhIGMJI5b33DGhtTb3wiag+kcHBQTz33HOYM2cOzj33XPz4448AAKvVipdffhlNTU3JGCNDhQ8+MKCnR/mPavduHsuWmRPS2WNwUN6qVIy1bG/nUVjow6xZbtTWhn75P/1Uj/ffDy9Y1YSdWkmpQkczursN/mXsE0/04sknAwI106pSiqq5GYaaGhhqasKGC9hsCBFPUle5ttaA1avloS9tbTzGjvXBbA44rNE0DgCAmTO9+P3vndqfkCAIoS1hE9VqNxY++2xwWI/PYDDk3HefE7NmJS7BM16ef96OZcsa8Ze/xFcCRixttWGDHp9/ri5YP/zQgO+/P7QrXqnEP/+pR3Nz6gl2zSEB7e3tuPjii9HY2IjS0lLs27cPNpsNAJCTk4OXX34Z+/btw3//938nbbAMOX193MEySqGC78sv9TjmGC/WrTNg/vz4+rAPDkod1oB46+ykDuuRR/qwfbsOXi9ktfe+/NIQsYyImsNKSkrANTeHPD40pgTd3QYYjTSznePkzx/ML0FGd+jzOADCnXcChIBz077VSuEC/f2hIQGBcyb48kt9yDmJTnNra8BhjaZxAEAFoyG8tk8KhAyvWAUQsZwXg8E4tAzHXBSOtDSgsNAd90qMKFgbGnjVxjQArat9ODusTifgSZ37FT+aP5GlS5eiq6sLtbW1qK2tBZFEQHMch0suuQTr169PyiAZythsnL8VaDDr1unx+OMOfPll/EU7BwbgL+yckSGtEsAf7GoCTJ3qxdatAbXq8QB79vB+x1ENp1M51tOxZAncRrlFaefS0Pu7JejpMagmCtXNexQuvfL6Nudy+cWq/7GgcIH+fg6n1ctbxZ7X8TYGBqi427pVF1IQu6ODOs3xOKzDhdMJWdIag8FgjFbEWqwNDbyqg0gITaRVK6V1OOBwcPAmpgN4QtH8iaxduxa33347TjjhBHAKlkx5eTlaW1sTOjhGeGw2+AvVS3E4qIg691wP9u7lEaTRomZwkEN6eqjD2tvLITub7nPeeR5ZWMDmzTqccoo3onvncHCK2fHuqir8eMfzsGYFqgc8Ov5lCDdXwWrVHzx26PO6fjUHq859UcFzVkcaflDy1Tu44IM7Za1ib/l+ITL/XoO6OtocIDj5qrOTQ0EBgSDAfwMRrcM6XHAcFN9HBoPBGG2IglVsha17JzRErKuLw9FH+9DUdPgKVqcTcLtTL05L8ycyODiIcePGqW53OBzwpqIkH6UQou6wfvutHtOne8BxwGmneeKOxQkWrGIMq3Q5+dxzPbIg9i+/1KOy0u3fTw01hxUABi6dg/ur6tFvtaJ94zZsGH8NdDr6esE9pUXS0oCvS68GKS3VfH7SzlUzPgtN2jJ5hjBpxaOordVj1iwPTCYiS3ZzuTiYTLRrk1gDdqQ4rCYThrVCAIPBYBwqcnMJDhyg1Qaquxch/fbbZOaEsHgxhl57V9a18HDE4eBGdkhARUUFtmzZorp9/fr1mDx5ckIGxYjM0BAVbU6FPJ1//UuHs86if22nn+7F5s3xCtZASEBeHkF7OweXC7IOU/n5BB4PzaIHgK++0mPmTA8yMggOhjorouawAvLez21tPMaNo2VWOI62hVVCEKgDrNilymAECQrOCu5cldOvnLSV1tWC2loDKis9/kxTQC7GzeaAwzo0FF0d1uHCZCJMsDIYjMOC3FyCLVt0+E9uJS5pfgVc0FocZ7ej/NVHUV7uQ1YW8V/PDjdGfAzrtddei1WrVuHDDz/0x69yHAeXy4XHHnsM69atw4033piscTKCsNlEJy/0LrCxkYfFQhVdQUH8/ZMHBgIOqyBQ8bp9O4+CAvmXfeZMDzZs0KO7m4NOB+TkRO7fHM5hlT63tZXzl1nJzvb4O28FI3a6cldVwb58ub8hwV6U4eubXoL9ReUmBSLd6crObH9OCc5ufhuTzjsOn3xuwsRzjoOhpgZ9ffAnaUkdVrsdw1oqSitGI3NYGQzG4UFuLsGPP+pw/c5HQsSqSEZPC8rLfQdbwh6elQIcDqRkDGtUrVnr6uowf/585ObmAgDuuOMOdHd3w+Vy4cYbb8S1116btIEy5PT3U2Gk5LA2NfGYMIEK1txcH6zW+FI+pSEBAFBZ6cHq1caQTijnnefBqlVGuN0cKis9B49P3cgJE5Qnh3AOa3Z2wMlsbeUxfjw9p/x8N4aG1ARrIFPfXVUFd1UV/vQnI/bu5eG1czi+yh62gcBbk6uxuG4BdI7Amr/bIGB92kVY2nIHeA8tq2JubwZZvBieBzkUFV0HALIYVrFcVKrDHFYGg3G4kJtL0NbGIwfKK2kA0JVWiooKHyoqvGho4HHiiSmo3JKM08nB40m9C5hmh5XjOLz44ov48MMPcfnll+Pss89GRUUF5syZgw8++ADPPvtsMsfJCCKcw+pwcH7XUrqsHivSslYAUFnpRk2NIaQ70ckn0/CDL7/U+2v4SZfPlQjnsOr1NOwBoCEBUsGqJrIEgWBoSH682loD7rvPic2bdRHb+32aOw8djy+XubD/d+MLOLHtUxg98hqAnN2Osc8t8wt3qcM6UmAOK4PBOFwQ5zpHYYnidsJxWF70OCZO9KG83IeMj9fE3Ob7/2/vzuObKrP/gX9uliZpmjQFurC0tEDZVxVZhEEBEUVlBMqiqDPiriAzI35hHNHB34yDMs6ILCODjs4MIsKIgAgubCKIKIKAbIVSaKG0FFK6JV2S+/vj8OPgRQAAIABJREFUetOkSdoUkt40/bxfL17aNG2fnpu2JyfnOU9TZrfjmjdrh0KdCevKlStx5swZj9uGDRuG119/HevXr8cnn3yCN998EzfffHMo10g+lJRIA+1rV1hrj3uqL2Gsizxk/1//1mPg5B6uH9ZevZxQq73Pf9dogE6dnPj6aw36Z34AU69e+PtCA4Y/1N3vD3pFhf8KK1CzqevcOQFt20r3qzthlfp7ZSUl0ttt24pIT3fi2LG6n6MVFwsQ7stAyaFDKLZaUXLoEApGTkQyvGe7AoDhYq7rCEOpwio0qTOvWWElouZCnohSPNvHHgdBQOVDD2G96V7o9cCNJz/A2E+e8tqU1RyS1srKJrjp6qmnnsLevXsbay3N1iefaBrcL3LlioBWrZxeOxnlc+1lZjPq3PTkj3b1ahhmzIAqJwcqiNBdqPlhVamktgBf57+PGFGFZ9v8B8aZ0scKEBFzyf8Pel0VVgBQqUQ4HJ4tAa1a+U9Y3auyALBzpwZDh1a71ubrRC537tVpWUyMiIt6/72t7hVWu13uX20aSaBezworETUPGo10DLbu19Ieh/PalJr9DMuW4cILf3XtSej0zh+hc3i/qhboMd9NWbj2sNaZsIpNqVTUhP3tbzpXT2qgSkqk2Z+1x1plZanQoUPNI03aUd/wCqt+3jwINs/xTu4/rK+8Yscdd3g/BZsypQpP5r5Q58e6q6uHFZBaGq5cEVBQILgSw1/84gqeeML/6V3uvaNbt9a0J/To4cSpU/V3wdTuPe3f3wHHyy94PyM3GLBh4DxXpdlgkFoCSkpqjrINd3Pn2tG7dxj+ZiIiCoFNm0ohCNIehwk3nEJervRKWlVGhkfrmfq8n2O+/RwbHkmafA8rhU5ZmYDS0oZ9jJyw1q6wZmWpkJbmZ+ZTA/j7oZRvj4sTfR7fp9UCmjz/Hyu3Gcg9QYNPr6yzwipPCnA6BdcJU3q9Ey1bBpYQ7tmjwYABUkKWkOBEQUHDH/JRUYDxEc+pA4XGFNgWLsSmFvciPt7587qkZ6ZNKWFt00b0OrmLiChSJSfX/G42m0WPYpF8zDbgOZ/bnb/bI4nd3kTHWvk61YqCq7RUcJ0eFai6Kqy1E1atVkSl/4KkT9fyw+r3Y+PiXG0Gck/QzCNPIG7Th34/l8UiIjdXuKqX2LOyVEhOdrrOqk9IEOs8P7q+FxSqMqTe1v3fX8GjozJRlZHh8QtOp5OemRYXC66XlYiIKDyZTKJrAzMgHTcut7rZ585Ftc77VTX3ud2RqLoaqK4Wmt6mK0DqY23dunVA/+o6CYv8KytDgxPW4mIB8fHePazZ2d4J69VsvPI5eD/AH1Z/HwvAq1VA7yxH3Gv+e4Li4kT89JPadWhAoERROm1r5Miap4k6ndRM7o90QEL9iab7fNjCQsFV7ZWf25WUACZTg5ZLRESNzGz2TFjdCxBVGRkoe71Wn2utud2RSN7IHY49rPXOYb3++uuRmpraCEtpvq62JSA52YmTJz0TMGkEled95QSr9tzUusg/lPp584CcXCC5Hexz5wb0w+r+sUJuLvKjkmFe+AIMjz7q8/6qc/57giwWEYcPq10TAgKh10vTE7Zs0WD+fM8EWRQ9j5R1V1wc2Ev5sbFSXy0Aj1aFhn4eIiJSjsmEWi0BKvTu7VZavC8Dv/r4Qfz1rzakpDSP3+lyESwce1jrTVh//etfIyPCn1EoqbJSqvq5V1ivXAHmzDFgyRIp2br//mj85z+euxVLSqRTrH76qebjysp8706/2lms8uD9YcNisGNHwzJq+WNFERh9cwx2ZJRKCWyO93ioutoM5ArrlCmB9zQYDFJF+fx5FVJTPeMh9SxJ47+WLtVh/vyanoqSEsE19qQuarWUqJaWwueGMSasREThz2SS/h7I8vMFr3GNU6ZUYcoUI5xO4JtvGlhZaoLkNsMm2cNKoSXPDHVPWIuKBBw5Il0amw3Yt897V0xxsTTWyr2HtaBA5fXDBtR/PGpdKiuBqKirT77cK5m+WgVsQnSdbQZxcSKOHVM1qCXAYJCmAwwc6P0Tl5gobbw6elSNrCzPh39DN0t9/bUGgwd7vm4iCNJLTOxhJSIKb75aAmq/EjluXBV27SpFVFT9+xwiQUWFtGeECSt5KS2VfljKympuKysTYLVKl6aoSHDdx11JifSD5d7DKj079E7sruXwgNrHsl6LqgzPnfbO5GTMa/ePOtsM4uKk77Fdu8DXEB0tYsMGrUf/qiwhQUR+voCsLJXHLyqgYZVRtVrE559rMGKEd2d6U5oSQETUXNXedGW1qmCx+Dv2W/Q4lCZS2e3S7HEmrOSlrEyASiV6JKVSwiq9bbUKKCvzfmZnt0tVPPcKa36+7z7Va6mwlpYCRuNVfaiLTidC3msl77SXT5Ha1OLeOj9WHmovz8YLhMEA7NqlwZAh/iusp0+rvGbflpRIBy0EwmIR8eWXWgwc6FlhVauBy5dZYSUiCndSS0DN3wFRBFR+sqLaI7AiVUWFtPm4yfWwWq3WxlpHs1VWJo2ncm8JKCuTqn3V1VLC6nAIPk+EMhjgcTTrxYsqnxVWi0XEyZNX99wkGBVWucJrMDT880jzXkXExzeswtqvn8Nnoi2Ptjp9WuX1bLkhFda4OBHduztcI7Nkej1w8aIQcOJLRETKcG8JqKoCNBr/v//l+7ZuHdnFCLtdgNHIHlbyobRUSqLcE1a52nrliuB6Kd/X2CudzrslwF+FtSEtAT/+WPOwkKYOXNsPqMUi4vJl6eufPSvg0iXp/wPpB7JYRLRuLfp91utLdLSIkSN9D5FLTJQS1oIClVdS2dCE1VfLgV4voqBAxZYAIqIw556wXrwo1FkYqd0+IMvMVIVlcne1KirYEkB+lJVJfae1e1gBqboqv5TvPvZKTvRMG1ZjzfcdXadGddizKiibrh54oKY0GYyWgNatnbhwQXqoLV+uw4YNUmG/ulo61KAuej3wj380rHFo8uQqTJniO2FNSHAiN1cFjUaEIHgmzdLQ6MASzUcfrcTEid6TC+QKKxNWIqLwJo21kv7ffQar//t6/x198kkDDh+OnFRKqrCKYTmHNXKi3ESVlUlV0do9rFqt6EpYo6JqV2CBX5a/D+PMGWhTddZ1atSvdj2JTns/8PoaDUlYRRE4f15w9caWl197S0CHDk7XjvysLBWysqSpB3a7NMy/PrV34tena1cnWrXyveaEBBE//KBGSoqI6GjR44lCVpYKHToE9rU6d3b6fNnfYJA2dbGHlYgovLlXWAsKak658ndf9xFYgFRU2rdPHVG9rdKmK6CqKvy+JyasCisrkzYClZd79rC2aSMlmUVFAtq0cXokrCUlAp7I+YPPU6NavzkP2tWrYerVy1V5Tfjyw4BbAsrKpAeqeyvCtSasqam1E1bp/ysqBOj1jZvYtWol/nx8rcNrpElubsOmEfii10vXp3a/MRERhRf3l/n9tdT5uq9s+3YNDAb4bBVoqioqhJ83XSm9Em9MWBVWWipvuvK8rV07J4qKpApr27ae7y8pERBv9x7ADwCq3BwYZsyAKifHVXmN+c0M3FqwMqD1uE8nAOTjSq/ue5PJFVb55fecHOlhF2iFNZjUailp7dDB6bHrUxQBh0OApt6jNOpmMIiIiRF9nqRFREThQ6cDbLaaCquvTcuy2hMFAOn479GjqyKuwnpL3kq8sS7dVfRqsWmT0ssCwIRVcXJLgOeUAClhlVsC2rVzerQMFBcLKNO18P0J1Wqvyqtgs2H6hT8EtJ7aCWtp6bVXWNu0EZGXp8KFC9L34nRKCaISFVZAagtIS3N6PbtOSgp8dJY/er3U60REROHNvbDQ0AqrKAL796sxbFh1RFVY075ZhQd2Pon48pp2w/Z//jO0q1crvTQmrI1t40YNdu2qObmqvBxo0UKE3S494LWrV+PFf3XGylV6PPKnLrju2AdITq5pCXjySQM23rcWhspir89dKUTBX6d0UlUOnAHkY94V1mtPWOUd/idPqpCW5kRSkhP5+YIiFVYA6NLFgfR0p0dLgNQmEIyEVWT/KhFREyEnrfn5vk+KlLn/vZgzR49f/CIG/fo5EBtbc/sHH2hxyy1G178vvrjGl+wUMOTTFxFV7bnRWW23Qz9vnkIrqtH0otnEffWVBt26OXDTTVJi6T42Srt6NQwzZiD65wppXHEOflfyBLZd58DxNpMBAIcPq/FD1PNQi9674O2aGGiSjBByvNsFCnTJqCgGLJa611dUJKB1a6dHwnqtY60AafD/rl0adOjghChKCaLBAEUqrG+/LcXXfYdosBJWgwGcEEBE1IQ4nUBOjvQKoD/uLWS7dmmwfXspVCqpj7WkRLrPvn1q/OlPdgwc6MCaNVocO6bCrbc2xncQPLFXcn3eLuT6vr0xscLayKSB9Z4zV+UKpn7ePK+X8w1iOYZuetF12tUd1vehyvXdv2qqssI+dy5Eg8HjdtFgwPs957mOe62L1SogNdUZ1B5WAEhLc2LLFg3S0pxIS5N6WpWqsMrce5Kys1Xo0IEVViKi5sRoFFFaClRWCl4HwbgzmeBKTAFpP4QgeP4dKSoS0LKlNDc8Jkb0eax6uCsyt/N5u9jO9+2NiQlrI8vKUrmavAHPl9z9PYOJseaitFSAuGI15p57HP5+BK7EtkNVRgZsCxfCmZwMURDgTE6GbeFCHO49OaDRVlarVGmUpwQEo4cVkDZe7dunRocOTnTo4MTp06qfT+9SLrmr3RIQjITVYAATViKiJsJsFpGdrUKrVnX//pf/XlRUSIf21L4dkAo+8nHiRqPo88CfcLdh0MuojvIsejn0etjnzlVoRTWYsDai6mrgzBnPI0HLyqSZZyqVCGdb389gbK3aSYntn+bB4PQ9RL9Sa8D34/4IAKjKyEDJoUMotlpRcugQqjIyAp7FarUK6NDBGdQeVgCuZDAlpSZhtduFsKmwnjmjQkpKcCqsbAkgImoazGYRP/6orrdgIf+9qH0ilvvfEatVgMUiQrt6NW59pDsWLjLA1KtXWGxYCtSu9pPx/WOLcEGX4ip6nfn971GVkaH00piwhkphoeB19GhurtQP6jlzVUoIo6OBoue8X863q6Jx+pGXUF4OaC/4rsCKAD4YvhQld030ux5/CWtRkeeZwd4Jq5RQX6sOHRxIThYRFSUlrceOqXH0qFrReaXyrk9RlF4OCkbybDCInBJARNREmEwiDh5U17uHQaeTToGqPf7KPWGtqhJgXCftRTEU5ECAtMveMGNGoyatTidcR6A3VEWFgMujJ2LcdadcRa/Lt98e5BVeHSasIfLMMwacOeP5gMnOVqFHDyfc21RtNmnIfEyMiMJRE2FbuBDntdIzm1x1Cv5x/RKUjc1AaakAWyvfFdiq1snYFDelzlM6LBbfCetf/qLH1q01e++KigR06OBw3beoKDinNiUni5g9Wzo+S6cD7rqrCjk5Am66SbnpxPJLOZcvC2jRIjhV0b59HRg92vexsEREFF4CrbAKgvSv9vgroxGuIpQg+N6LIthsjbrL/uBBFZ599uqqQXY7eDRrc1NWBq+G66wsNXr0cHhUWAHpQS73u1RlZOCO7lkotloxumsWfuoz+ecjRAX8MP4lVGqiPT7Wro7G2cdexMWLdZ+D7L/CKuDUqZqHgbzpqqhIqjxWVQnQaq8mAp7UamDKlJpEbs6cCrz+uh033qjcT4XZLDXRnz4d+JGs9UlNFTFoUBj+pBMRkReTCTh0qP6EFZA2PhcUeCas8lgspxMQBNHvXpTG3GV/5YqAU6fU9d/Rh4oK6VVVnnTl5pVXXoHFYvH417lzZ9f7RVHEK6+8gq5duyIpKQljxozB0aNHlVpug9lsglfDdVaWCj16ODw2XcmMRqC01PO2uDgRFouImBgpAd7ffTK+yFjksaHqX4OW4NzNE3HpUt1VQn8Ja0mJgNOnVR5vx8YCTqeAS5eEehvRmzL5pZxgbbgiIqKmxWQSYbNJhZr6CAKQl6fy+WpmcbFUBPG3m74xd9kXFwvIzlZ5tSUGwm4XYDCIcDjCb8OYohXW9PR0HD9+3PVv9+7drve98cYbWLx4MebPn4+tW7ciPj4e99xzD0rc50qEsbIy3wlrz55Oj01XMrnCWl0NqNXSo8xikRJW+X1FRQLyR07y2FC1v9tkVFQIcDoFqOt4QhUXJ7p2/rurnbACNc8Ypcpj5CZycktAsGawEhFR02I2i2jTxola20d8iokRcfq07wMGrFYVLBbR72jJxtxlX1IioLhYuKo+1ooKBBQLJSiasGo0GiQmJrr+tWrVCoBUXV26dClmzpyJsWPHonv37li6dClKS0uxZs0aJZccMJvNu2J67pwKnTrVtATILyEA0g+ClOTWzD2NixMRFydtVKqsFDxGZsj0eql/Rk5y/fFfYQUuXKh5GMjPyNRqESdOqAJ61tlU6XRSgzkTViKi5slkEgP+/W8yiTh1yrvCajCIyMuT/j67j5Z0oma0ZGPusi8uljZzZ2U1PMWTpveE56QbRRPW7OxsdOvWDb1798ZDDz2E7OxsAMCZM2eQn5+P4cOHu+5rMBgwePBgfPvttwqttmFqtwQ4nVIyGBMD16ar8vKaZzLR0dLL/u5jpFq1cqJly5oHjq+EVacTkZsroFWruh9gFovvCqtUmRVRXY2fB/nXVHcPHAisr6epO3MmshNzIiLyzWIR0bFjYL//zWYRJ0+qPHpYASmRPXtWhbg46fPIoyWv61OFkkOHkD14IrZsabyDRUtKBPTq5biqhFWaMxuCRQWBYkez3nDDDViyZAnS09NRWFiI1157DaNGjcKePXuQn58PAIiPj/f4mPj4eOTl5dX5eTMzM0O25oZ8vdLSvjh9+iIyMy8CAAoKtDCb2+PUqZOw27shMzMThYUaCEIaMjMzUV6egKwsB4zGUohiG2RmnsY996ig1TqRmQlUVHRDTk4FiopykZlZ6fo6ZWWtceCAAL0+CpmZ2XWutaiom9d6Kyq6oXXrSnz9dQ50OhFRUe2RmXkSGk0H7N4dhREjspGZab+2IIVAsK5zRUU32GwCzp9v3MdNU9bYP2NUg7FXHq+BckIR+5YtgYceUiEzs/6k1elMQVVVC5w757kOQUjDDz/YodOJyMy84Lq9okL6m7t1qwUffJCIlJTGeeycPdsO7dsL2LfPgeuvP9+gjy0v74bTpzNda5c1xuM+PT29zvcrlrDeWuuA3RtuuAF9+/bF+++/j/79+wMABMGzIiiKotdttdX3DQdTZmam369XUaGG0ZiA9HQLACA/X40ePbRIT0+HTqdHeno61GoVEhJ0SE9PR2qqFuXlAuLjq5GUpPP6vDqdHlVVOvTr1x4WS83tbdpE4fBhNTp1ctb7vctf111UlB69e2sgih1hsTjRtq30tVNS9PjqqygMG5Ycdv0sdcW9oXQ6PYxGsVEfN01ZMGNPDcPYK4/XQDnhEPvkZB1atxa81tGmjR5lZSb07FmN9PSaQdzR0TqkpaXDYNDi0CEDEhPTYTaHfp0ajR4DBzqwY4cG6ekNO1tdzhPc84VwiD0QRmOtYmJi0LVrV2RlZSExMREAUFBQ4HGfwsJCr6prOHI4pN5I97FWvnail5bW9KvKPaz+jkJVq0Vcvix4Pdj1eiAnx/slCn/cdw1WVgJarYi0NOmlA/eWg7g4ES1bimGXrAZbTEzg/UtERNR8mUzw+bfWZALOnpU2XbmTW/2sVgHt2jnx1VeNUyMsLpZaAmpvqG7qwua7sdvtyMzMRGJiItq3b4/ExERs27bN4/3ffPMNBgwYoOAqA1NeLh216t7Dmp3tnbC696sajTU9rNHR3j8Q8tgrVa0rptNJvTN1zWCt+Rwiyspq3i4tlQ4FSEtzeiWsFkvzSORMJrFZ9OkSEdG1MZtFnyOtzGYRZ86ovPaYyBN+rFYBEyZUeRzSE0olJdJcdrs9/EZTXQvFEtY//OEP+Prrr5GdnY3vv/8eDz74IMrLyzFlyhQIgoAnnngCf//737F+/XocOXIETz75JIxGIyZMmKDUkgNmswlo2dIzYa1dYRVFKTmNiZETVhGlpYLHbe6MRtErWQWkCuv580Kdp1zJak8KKC6Wnhl26OBEZqYKFy6oPCqszSGRM5ubx/dJRETXxmQS/VRYRZw7570pWn7l1GpVYdSoanz7reaqZqM2VEmJAJNJRGysiKKi0H+9xqJYwnr+/Hk8/PDD6N+/P+6//35ERUXhiy++QEpKCgDgmWeewZNPPolZs2bhlltuwYULF/DRRx/B1AQOai8vBx7UrsBrq9NhjouDqVcvdN+/CsnJUmJkMIiw26WKaUyM9DHx8SLOn1d5tAm4i4kRvX4YAKnC6nTWfcqVzDthlR7UbdtKH/vRR1r06iWd0tSzpwO33hr5R4wOGVKNfv3C8EgPIiIKK926OTBkiPffC5NJGrTvXWGtaQlISHCidWsnLlwIfdWzvFyA+ZPV+OiHjkhJk3IQ7erVIf+6oabYpqt33nmnzvcLgoA5c+Zgzpw5jbSi4DGsXY2XL8yA3imdECDk5OAPwuMQ11WgKiMD0dEiysulZ13yA7xTJydOnlThxht9n1gVHQ2fCav+5+OCA62wuo+2KimRWgJUKuDDDz1PM+jTx4k+fSK/8vjAA5GflBMR0bXr3duJ3r19twQA3n+j5VdOrVYBFouINm1E5OcLaN06tGXW2y+/j+hnZsD48wxNIScHhhkzAKDOebBOp9TOGK7Cpoc1kiQv/aMrWZUZxHLo582T/t8gVWGLigTX3DZBAK67zoGvv9b43HRlNPqusOr1IvR6MaCdh/4qrERERHR1zGYRUVHem5TlHtaSEunY1oQEJ/LzQ592Tb/wBwjywPefCTabKwfxp6KipggWjpiwhoD+Yq7P24Vc6fboaOnsYvlZl2zkSKkpW24TcGc0il47EAFpwG9Cgoh6pn0B8D48QO5zISIioqtjMkkFodp/h+UeVlEUoFIBiYlShTWURBFIqsrx+T45B/FHOjSgJidojH7bhmDCGgLlLdv5vF1sJ90uV1hrJ6w331yN6mr4rLDGxPhOWPV637sWfaldYS0pERAbG2aPSCIioibEbPb9Cqg83UeWkOBEQUFo067yciA/Ktnn++QcxB/pWFbp/7Va6QTMcMKENQT2jX8Jldpoj9uqowywz50LAG49rJ5N2hYLcMMNDr9jrfxVWOPjA0s64+KcuHy55pJLLQEBfSgRERH5YDL5LigZjSKuXBGg1UrvS0wUUVAQ2gprSYmA97q8DLFWf4JoqMlBfNm6VYOlS3WuhFWjARPW5uCnPpOxedwinNemQBQEFBpTkPncm65mZ/eWgNrPyhYvtqF7d++K6T33VGHSJO8NQunpTrz8cmBHp3bt6sSPP6pdb5eUgC0BRERE1yAhQcQbb9i8bjcaReTm1hwo0FgJ674uk2FbuBD2xGSIEOBMToZt4UK/G65EEXjhBT369avG//2flE+o1UxYmwWbTcCZmybh9m5ZKLZacd9NJ6GaWvNAkVsCpB5Sz49NT3dC42N2Q6tWIlq18k4uNRppwkAgkpJElJTUvEQhTwkgIiKiqyMIQJcu3n+HY2KA3NyaaUDx8aHfdFVcLP1dr8rIwInPD2PsXRUoOXSozukAJ06o0LGjE7/8ZTV69JC+D41GOrUznDBhDYHycikpjYoSUVkJXLwoeCSbBoNUYQUQ0GapYBo61IGdO6WMmFMCiIiIQqN2hTUmBh4HCoWC9Mqp9P8Wi+e+FX+2bNFgxAjPV3C1WhFVVeF1UhYT1hCw2QQYDOLPQ4MFOByCR9U0OlpKahs7WQWAESNqjodjwkpERBQaUsLq3foXSu5/181m6ZXU+mzZosHw4Z6v/7MloJmw2aRNUkajiOJi78Q0OlqE1aryGB/RWAYOdGDPHilhZUsAERFRaMTEAJcuqTwS1uhoEWVlofuacksAIOUe9Y2mstmAwkIVkpM978hNV81EWZlUYY2JEeH4zxpsPtbBdUSrdvVqGAwizp9v3GddsqgooG1bJ7KyVB5HwxIREVHwyBN/3P/WJySIuHgxdC+v1p6vXt8ruT/8oMaNN3pnpuxhbSbkloDhF1ai5xtPI6niLARRhOrn49G67FuF8+dVPsdgNIaRI6uxZYvGNcyYiIiIgkueqe6esCYmhnbjVe2ENSpKREWF//tfuiQgMdHXhm4R1dXsYY14NpvUp5qxfy60Vd7Ho/Va+ZJiFVYAGDGiGl9+6WMUAREREQVF9M/j2GtXWEM52sq9JUD+2u4nXNbm78RLjQao8p6kqSgmrCFQXi4gOlpEXInv49EMhbk4d06lWMKalubE+fMqOAObhkVEREQNpFJJVdbaFdZQnnZVe29KfZMC/G2+Zg9rM1FeLj2zuhLr+xi0ysR2KChQriUAAAYOrIZezw1XREREoWI0ep6ClZAgIj8/lD2s8JjvXvtIdu/7+958zR7WZkLuYd195x9RDs8jWkWDAfkzpOPRlKqwAlJbACcEEBERhU7thDXUp13VTkADSVh9V1jZw9os2O0CdDog9xeT8DCWoSIpGaJQczxaZcZEAMomrDffXI1nn62jE5uIiIiuyfz5dmi1NW/HxTlx6VLoUq/SUsG12QsIrCXAbPa+Xa1mD2uzIQjSM6uVuA/ndx9GsdXqOh7N16iLxqbXA4MGhVm9n4iIKIKMGuXZCBoTI7UNhorNJp20KYuLE9Fm+yqYevXyGK8pk1oIvHMRrTb8eli5VTwEBEG6+EajdLxZ7V5V+cGkZMJKREREjctoFEN+PKv77NXehz/AdWunQ1UtZcnCz+M1AaAqI4M9rM2dKEqPFqNRREKC6DXrVKUC9HqRCSsREVEzotcDdnvoPr+cf8j6rHoJUdWeJV3BZoN+3jwAdU0JYA9rsyIlrL5nR0VHi4iNZcJKRETUXNR38lQjtCuEAAAgAElEQVSw6S/m+l5HrnS7zSZAr/d+v1odfi0BTFiv0pkzAs6c0Xnd7nAAKlVNj2q7dr6T0vR0J9TqkC6RiIiImrHq1r7Ha4rtpNsFwXcSzYMDIkh2tgqrVyd43S6fcgUAbdqIePdd393Vn35aFsrlERERURiq/bJ9sDidNQUzme2FubCpvMdr2ufO/Xktvj+XVsse1ogxcKADBw7EeN1uswmuKQAA/FZRWV0lIiJqfgRBDMlJkzYbvF7eF6dkYF7bf8CZ7DlesyojA6Lov0UhHHtYOSXgKul0QHx8FbKzVUhNrUlQy8o8R0oQERERyQwGKbk0GoP7eWsXzGSbWtyL2Tvu9rq9rAweM1vdsYc1wgwcWIytW7Uet8mnXBERERHVFhMTmtFW5eUNK5j5mxAASD2sTFgjyKBBV7Bli2eR2t8zHCIiIqJAZrEWFgo4dUrVoEMG/OUfKpXosx/V3wxWIDwTVrYEXIPk5AqcOeOZ85eUBL/MT0RERJHBaARKS+u+z+TJ0UhIEBEdLWL5cltAn7e83HfCGhMjfb3YWM/b605YRVRWhlcPKyus10AQpJOs3J+5FBUJPBCAiIiIfAqkwlpdDaxYUY4jR9RQr1rt92hVd/5aAsxmEcXF3l+vpIQtAc1Kq1YiCgtrHghMWImIiMifQHpYRVGAIABPWv4Lw4wZUOXkQBBFqH4+WtVX0uqvJcBkElFS4v31iosBk8n312fCGoESEkTk59c8EKxWJqxERETkWyAtAbIHj78AdYVnS4D70aru/FVY/Sesdfewcg5rhElMdKKgoCaMVqsKcXEhGLBGRERETV4gLQGu+16u+2hVd/56WGNjG94SoFaLqKpiD2tESUgQUVDgWWG1WFhhJSIiIm9Go4jy8sCSQfkI1UBub3hLgP+EVatlS0DE8ZWwsiWAiIiIfImJkYb2++NwSKdhAYB97lxUqP0frerOf0uANMGotqY21ooJ6zVKSHAiP9+9JYAJKxEREflmNIooLfVfYbXbaxLPqowM/O+2xShr5X20am3+Di4yma5uSkC49bByDus1Skz0rLCWlvrfdUdERETNW3R03T2sdrsAvb4mkTx38yT86/aJuP/+qjo/b3k5EB3tfbvZLOLECe/6ZHExYDb7/lxqtYjqavawRpTaFVZ5FAURERFRbfWNtbLZAL2+5u3am7v98bfpyl8Pa30tAVV158eNjgnrNTKb4bPUTkRERFSb0Vh3D6vd7vnSfu3xmf7UtemqoS0BWm34tQQwYb1GggBXRbWiAoiKYv8qERER+Sb3sJaWSkew1uZdYfVsPfTHZvN/0pV7hXXFCi2uuy4GGg2g0/n+XOG46Yo9rEEQFSWiooKnXBEREVHd5Dmsp0+r8O23aq/3166wxsd7th76U17ue9OV2ew5JWD9ei0++qgMqan+8xX2sEao+Hjp2Q9nsBIREVFd5JaArCwVrFYVrFbPxLB2hVUag1V/8uhv01VMTE1LQEUFkJenqjNZBcKzwsqENQjkhmiOtCIiIqK6SCOjpAprbKyI06c9U7HaFdZAN3L762GVDgGQPsmePWoMHFh/JsqENULJDdGssBIREVEgsrLUuPnmamRleaZitSusAGAwiCgvr/vz+fo4mZz0fvmlFiNH1p+J8qSrCCU3RLPCSkRERIHIylJhxIgqV8J68KD039pzWAHvUzV9EUUBqnqyuq+/VuOmm+rPRNnDGqG6dXPgwAE1N10RERFRvQRBxJUrAq67zoGsLBUuXxYwbpwRgOdJV7JAZ7H6o1aLOHtWQGysCKOx/vuzJSBC3XCDA999p2GFlYiIiOql1QI6nYi0NCeys1XYtk2DK1cEiKLvCmt8fGCzWP0xmYB167QYMSKwLDQcj2YNm4T1r3/9KywWC2bNmuW6TRRFvPLKK+jatSuSkpIwZswYHD16VMFV+qbVAh06OLF3r4YJKxEREdXJaBSRmur8eWKAgC1bNNDppOpqKCqsJpOIjz9uWMLKk658+O677/Dee++hR48eHre/8cYbWLx4MebPn4+tW7ciPj4e99xzD0rcB4qFiZEjq7Bzp5oJKxEREdUpOhpIS3MCkMZOffedGjfeWI2SEgE2m+8e1roqrE6n1Gbgj8kk4sIFFbp1cwa0Po2GPaxerly5gkceeQRvvvkmLBaL63ZRFLF06VLMnDkTY8eORffu3bF06VKUlpZizZo1Cq7Yt+HDqyGKbAkgIiKiusXEiOjQQUoe09Kc6N3bAYtFOpHKd4W17k1X/k65kpnNIm65pTrgEVnsYfVBTkiHDRvmcfuZM2eQn5+P4cOHu24zGAwYPHgwvv3228ZeZr2Sk0WMGlWF2FgmrERERORfu3ZOdO8uNYled50Dd99dBZMJKC6WKqw6nWcu0bq1E7m5/lM2m833KVeytDQn7r478Nf4o6KAMWPCqydA0aNZ33vvPWRlZeGtt97yel9+fj4AID4+3uP2+Ph45OXl+f2cmZmZwV1kPdy/3p/+BJw+3ahfvtlq7OtMNRh75TD2yuM1UE4kxX7KFOm/mZmAXK/74ot2OHr0CvLzW+DixYvIzKwZvCqKwJkz3XHkyElotd6JaV5eFJzOZGRmnvL59UaMqPl6gbrttpr7N0bs09PT63y/YglrZmYm5s2bh02bNiEqKsrv/YRa9WtRFL1uc1ffNxxMmZmZjfr1SMK4K4exVw5jrzxeA+U0h9inpOhgNpsQFaVF5856pKd79pv+4hdqXLrUBUOHem/fdzpVSEjQhSRG4RJ7xVoC9u7di0uXLmHQoEFo2bIlWrZsiV27dmH58uVo2bIlWrRoAQAoKCjw+LjCwkKvqisRERFRU2YyyT2svl/eHz68Glu3+q4zlpcLdfawRgLFEtYxY8Zg9+7d2Llzp+tfv379MH78eOzcuROdOnVCYmIitm3b5voYu92Ob775BgMGDFBq2URERERBZzaLKC6WNl35OmL1F7+oxldf+UtYUWcPayRQrCXAYrF4TAUAgOjoaMTFxaF79+4AgCeeeAJ//etfkZ6ejk6dOmHBggUwGo2YMGGCEksmIiIiCgmTSURensrnWCvp/dI4rM8/1yA11YnOnWtaBmw2AdHRTFgV88wzz8Bms2HWrFkoKirC9ddfj48++ggmk0nppREREREFjdnsf6yV7Lnn7Pj0Uy127tTg669LXbeXlgIxMY20UIWEVcK6ceNGj7cFQcCcOXMwZ84chVZEREREFHpmM1BcDFRWCvC3F33oUAeGDnVg2DDP7NRqVUX8HHjF57ASERERNXfypisAAQ34F93yU6tVQFxcYKdYNVVMWImIiIgU5p6w1sdoFFFWVvO2lLCywkpEREREIWQySVMCAhEXJ8Jqrbmv1SrAYmHCSkREREQhFB0tzVMNRO2EtaiIFVYiIiIiCjFB8OxLrYuvCisTViIiIiIKuepqQKutP/G0WEQUFdUkrKWlQsSPtWLCSkRERBQGBMH3KVe1xcV5JqyiGNhkgaaMCSsRERFRGDCbxYCOWK3dEtAcMGElIiIiCgMmkxhghdUJq1VK4Ww2+DzKNdKE1UlXRERERM2V2SxCFUAp0WKpqbA2hwkBABNWIiIiorBgMolwOOp/qd+9JaCoKPJnsAJsCSAiIiIKCyYToNM1rIe1OYy0AlhhJSIiIgoLZrMIoP4Kq8kE1zGuTFiJiIiIqNGYTCIqK+u/n/shA80lYWVLABEREVEYMJlEGAwN+5jmkrCywkpEREQUBsaMqUJVVWDzVXU6ERUV0qarbt2cIV6Z8piwEhEREYWB2FgACKxaKp921VwqrGwJICIiImpi5FmszWUOKxNWIiIioiZGHm1ltXIOKxERERGFIfcKa2wsE1YiIiIiCjOdOzsxb54eGg2gViu9mtDjpisiIiKiJmb8+CqMH1+l9DIaDSusRERERBTWmLASERERUVhjwkpEREREYY0JKxERERGFNSasRERERBTWmLASERERUVhjwkpEREREYY0JKxERERGFNSasRERERBTWmLASERERUVhjwkpEREREYY0JKxERERGFNSasRERERBTWhKKiIlHpRRARERER+cMKKxERERGFNSasRERERBTWmLASERERUVhjwkpEREREYY0JKxERERGFNSasRERERBTWmLASEUUwq9Wq9BKIiK4ZE1aFiCLH3zam6upqVFRUKL2MZunIkSPYvn270stoljIzM3H33Xdj/fr1Si+lWePve2Ux/soJZuyZsDaivLw8fP/99wAAQRD4Q9RITpw4gZkzZ2Ls2LF49tlncejQIaWX1GwcOnQIQ4YMwd69e5VeSrNz8OBBDBs2DIcPH8Z3332n9HKapePHj+N3v/sdJk6ciJdffhmfffaZ0ktqNnJycrBhwwY4nU7+vW1koYq9evbs2S8F5TNRnU6cOIEhQ4bgwIED6NatG9q2beu6kIIgKL28iPXTTz/hzjvvRGpqKnr27InVq1ejvLwct912m9JLi3gHDx7E6NGj8fjjj+MPf/iD0stpVg4dOoTbbrsNs2bNwuTJk7FgwQIMHz4cSUlJSi+t2Th+/DhGjRqF1NRUdOrUCbt27cKnn36KwsJCDB06VOnlRbTMzEwMGzYM+/fvR6tWrdClSxeoVCr+vW0EoYw9E9ZGUFhYiCeeeAKdO3dGbm4u9u7diy5dujBpDbGzZ89i4sSJrj/Yw4YNQ0xMDE6ePIlbbrkFOp1O6SVGrNOnT2Po0KGYMWMGXnzxRVRWVmLNmjXYuHEjTpw4gaioKCQkJCi9zIgkP1F48sknMXv2bDgcDmzatAlt2rTBDTfcAIfDAZWKL66Fks1mw//93/9hyJAheP311zFs2DCMGjUK//73v7Fx40ZcvnwZt956q9LLjEiXL1/G9OnTkZ6eDq1Wiz179sBoNKJr165MWkMs1LHnb61GcObMGbRr1w7PPfccNm3ahPz8fMydO9f1Mh1/eILP6XRix44dGDVqFJ5++mnX7ceOHcORI0dwyy234Fe/+hWWLVum4Cojk9PpxIYNG6DX62GxWAAAU6ZMwaJFi7BhwwbMmzcPzz33HFavXq3wSiOPzWbDtGnT8Oijj+KFF14AAPTt2xdDhw7FkiVLYLPZoFarFV5l5IuKikJ+fr6rol1RUYGEhAQMHz4cY8aMwa5du/Dee+8pvMrIZLVakZKSgscffxzvvvsuYmJi8NZbb2Ht2rVsDwixUMeeFdZGYDQa0bFjR/Tp0wcxMTEYO3Ysli1bhu+++w6dO3dG27ZtAYCVjyASBAFJSUlITU1FWloaAODVV1/F4sWL8dRTT2HkyJE4c+YMvvrqK/Tt25cvlQaRIAhITk6G0WjEf/7zH8yfPx/t27fH4sWL8dvf/ha//OUvsWXLFhw4cABjxoxhpTuItFot7rjjDtx9990ApM2GKpUKSUlJWLduHYxGI/r168cqUwg5nU4UFxfjP//5DywWC4YPHw6tVovc3Fy8/PLLeOCBB5Cfn4/s7GyMGzdO6eVGnJiYGHTr1g19+vRBdHQ0RowYgc2bN+Obb75BdHQ0unXrBkEQXD8bFDxGozGksReKior4VKMROJ1OqFQqVFZWup5933777UhMTMS8efPQu3dvvPXWW0hKSsLEiROVXm7EkONeUVGBhQsXom/fvq6X4rKzs9G/f38sWrQIkyZNUnilkefcuXP417/+hSNHjmDOnDno1auX63379u3DyJEjsXnzZgwYMEDBVUYmh8PhUUktLy/H+PHjYTQasWbNGgVX1nysXbsWDz30EEaNGoW2bdti1apVmDBhAt544w3s3LkTU6dOxe7du9GmTRs+eQgCX0/CqqqqoNVqcfnyZUybNg1lZWV47LHHcMcdd+D111+H2WzG9OnTFVpxZJD/xsrxl3/3yLlOMGPPhDUEsrKysGHDBhQWFqJXr14YNWoULBaL68JWV1dDo9G4ktakpCSYzWZs27YNO3fuROfOnZX+Fpqk+uLu/oMFSDsZH374Ybz00ksYPHiwwqtv2mrHfuTIkWjRogXy8vKQnZ2N66+/HlFRUa7Yf/vtt5gxYwY+/PBDpKamKrv4Ji7Qx/2OHTswadIk/POf/8Rdd92l9LIjivs16NmzJ0aOHImWLVvi008/xX//+1/odDrceOONeOKJJwAAa9asweuvv46tW7dCr9crvPqm7eLFi3A6nUhMTPSZtMoJlJw42e12CIKAH374AVu2bEGPHj0UWnnTd/LkSbz33nuwWq1ITk7Gr3/9a4+9CXKuE6zYM2ENsqNHj2L06NEYNGgQTp8+DY1GA5vNhlWrViE9Pd3rWUhOTg569+4Ni8WCdevWoXfv3kp/C01SfXEHvJ+B/7//9/+wefNm/O9//0NiYqJSS2/yfMW+vLwcH3zwAbp06eJKmNzNmzcPu3btwgcffIC4uDiFVt70Bfr7BpCeoD344IO47rrrsGDBAoVXHjn8Pf5XrlyJrl27oqqqChqNxuN3z5w5c3DixAn8+9//htFoVHD1Tdvx48cxefJk9O/fHy+++CLatm1bZ9J64cIFDBgwAIIgYMOGDR6v+lDDHDt2DKNGjcLIkSNx8eJFlJSUICcnB2+99RZGjBjhugbBjD0T1iCqrKzE1KlTER8fj8WLF8PpdGLfvn2YP38+fvjhB3z00Ufo27ev6wLabDa8+OKLeP/99/Hll1+ia9euSn8LTVKgcZd/kR0+fBhr167FP//5T2zcuJG/tK5BXbHft28f1q5d6xH7gwcP4qOPPsI777yDTz/9FD179lT6W2iyAn3cy1UOQHqisGLFCvzwww9MlIKgrmvw/fffY+3atejXr5/rd/7Ro0fxzjvvYNWqVXz8X6Nz587hgQceQHFxMSwWC3r16oXf/va3aNeunc+k1W634/e//z0+/PBDfPHFF+jWrZtCK2/6HA4HHn30UQDA22+/DVEUceHCBbz88sv4+OOPsXTpUowdO9Z1HYIVe3YcB1F1dTUuXryIPn36AABUKhVuuOEGLFq0CAMHDsSECROQl5cHtVoNURSRl5eHffv2Yd26dUxWr0GgcRcEAefOncOCBQvw5ZdfMlkNgrpiP2jQII/Y5+bm4pVXXsHnn3+OjRs38o/1NQr0ca/RaFBdXQ0AeOyxx7BlyxYmq0FS1zUYPHgwMjIyXL/zi4uL8dNPPyEnJ4eP/yDYu3cvTCYTli9fjrvvvhv79+/H66+/jtzcXAiCAKfT6XF/u92OEydO4OOPP2ayeo0EQcDFixddG5oFQUDr1q2xZMkSTJo0CU899RR+/PFH13UIVuxZYQ2y8ePHQ6PRYNWqVR63Z2dn4/HHH0erVq3wzjvvICoqCg6HA3a7nX88giCQuL/99tvQ6XTIzs6GXq/nZIAgaUjss7KyoNfr0aZNG4VWG1ka8vuGQqMhj//S0lKIogiTyaTQaiPHlStX8P3332PEiBEAgIULF7oq2r/5zW+QnJzsVWmVN2HRtXvkkUdw/PhxbN++HSqVymOz1YMPPojz589j8+bNMBgMAIITe461CrLS0lLs3LkTUVFR6NGjh6t3z2KxoKysDFu2bMGECRMQHR0NlUrFPyRBEkjc5V3SFosFMTExCq84cjQk9nFxcfxjHUQN+X1DodGQaxAVFcUxbkGi1+vRoUMH19sDBgxASUkJtm3bhuzsbPTo0QOxsbFYunQpOnXqBIPBwBnEQSA/CTAYDNi+fTvy8/MxYMAAaLVaOBwOaLVaGI1GrF+/HrfffjtatmwJAEGJPVsCgmzSpElo27Yt3n77baxbtw4VFRWu9/Xq1Qt2ux2lpaUKrjAyBRL3srIyBVcYuRh75fD3jfJ4DZQnv/w/ffp0jBs3ztUe8Nhjj+H555/H5cuXFV5h5JAr1kOHDsXgwYPx+eefex1K0r59ewDw+FkIBiasQVRZWek62aFFixZYtGgRFi5ciIqKCpSUlGDz5s0wm82u038oOBh35TD2ymHslcdroBz3HlV5XCQgJa333HMP/vvf/2LTpk3Yvn07OnbsqNQyI1JlZSX0ej1efPFFXH/99fj4448xa9YsFBUVIS8vD//73/+g0+mC3nbHHtar4L7rVib3b+Tm5iI3Nxe9evXC888/j2+//RanT59Gjx49kJ2djY8++sjVoE8Nw7grh7FXDmOvPF4D5dQV+/z8fOzbtw933HEHgJoh9s899xw+/PBDbNq0iRusrkFRURF0Op2rDxWoif3Zs2exf/9+3H777ViyZAlWr16No0ePomvXrrh8+TI++OAD9O3bN6jrYcLaQJmZmVi2bBmeeeYZtGvXDoDnBRw9ejR+/etfY9asWaioqEBeXh6++uortGrVCj169HCVyqlhGHflMPbKYeyVx2ugnEBi/+ijj2LmzJmuj9m0aRPuvfdebNu2LegJU3Ny9OhR/PKXv8Srr76KsWPHAqh5QiDHfvz48Xj55ZddG8i3b9+OFi1aIDk52XW9gklT/11I9tNPP+HOO+/EuHHjYLVaXfPe1Go1zp07h/79+2Pq1Kl49tlnIYoidDodUlNTeZLPNWLclcPYK4exVx6vgXICjf0zzzzj8XG33347jh49yikw1+DgwYO46667YLPZ8N5772HYsGGwWCxQqVQoKCjAyJEjceedd2LevHkApA1VRqMRY8aMCem6OCUgQJcuXcLkyZMxZswYvPbaa66Tka5cuQK9Xg+73Q6NRoM//vGPUKvVPBs6SBh35TD2ymHslcdroJyGxl7mcDigUqlgNBp5Pa7SoUOHcNttt+Hxxx/Hvffei/fffx/jx49HixYtAADl5eUwGo144YUXGj3GbAkI0IEDB/Dss8/ik08+QVRUFB5//HHk5ubi2LFjmDp1Kh5++GGkpKQovcyIw7grh7FXDmOvPF4D5TD2yvjxxx8xfPhwzJw5Ey+88AIAoH///ujTpw+WL1+u8OrYEhCw4uJilJeXw+FwYNy4cdBqtZg0aRJKS0uxaNEi5Ofn48UXX+RA9CBj3JXD2CuHsVcer4FyGPvGZ7fbsXz5ckyfPh0vvPCCawrDhAkTsG7dOmRmZiI9Pd3nsbeNhQlrgGJjY5GdnY2NGzciKSkJzz//PJKTkwEAPXv2xJQpU3DrrbdiwoQJCq80sjDuymHslcPYK4/XQDmMfePT6/V4/vnnXb2/giBAEARMmDABb775JjZu3IiZM2cq2mrBOax1cDgcrv/v06cPxo8fj9/85jfYsmWL63an04lhw4Zh4MCB2LNnjxLLjDiMu3IYe+Uw9srjNVAOY6+cqqoqAPDYqCYIApxOJzp27Ihp06Zh5cqVyM7OVmiFEiasPhQWFgKQdr65/xA98MADGDJkCIqKivDTTz8BgOsYPo1Gw56aa8S4K4exVw5jrzxeA+Uw9sqRYy8fq1qbHO9Bgwbh/PnzyMzMBOB5aENj4pSAWo4fP44+ffrgxIkTGDt2LFQqFaqqqqBWq9G2bVskJCTg7NmzePPNN2E2m5GVlYU1a9bgs88+w7x581w76ahhGHflMPbKYeyVx2ugHMZeOb5iL09ZqK1Tp0747rvvsGHDBtx7773QarUKrJgJq4e8vDw88sgjaN26Nb7//nscPXoUd911F9RqNSorK6FWq5GamorBgwfDYDDg3XffxYEDB1BQUIDly5fzRI2rxLgrh7FXDmOvPF4D5TD2yvEXe19Jq9PphCAIyM/Px8GDBzFhwgRER0crsm6OtfqZKIqus4effvppFBQUYMaMGRg9ejSWLVsGQDo/NyoqyvUxeXl5MJvNcDgcMJvNSi29SWPclcPYK4exVx6vgXIYe+UEEntfx+HabDZcvnwZbdu2VWLZAFhhdREEAe3atYPZbMatt96K9PR0dOzYEW+++SZ++ukn3H333VCr1aiurnbtnjOZTIiKioJOp1N6+U0W464cxl45jL3yeA2Uw9grJ5DY1660iqIIrVar+BMFJqxuoqOj0aVLFwiCAJVKhbS0NHTu3BmLFi3yuJAffvghYmNjERsbq/SSIwLjrhzGXjmMvfJ4DZTD2Csn0NivWrUqrGLfrFsCcnJycOTIEeTn52PUqFEwm82Ijo6G0+l0PbOw2+347LPPMH36dIwePRrx8fFYsmQJDh486JoLRw3DuCuHsVcOY688XgPlMPbKiZTYN9uE9fDhwxg3bhwSExNx5swZxMTEYPz48Xj44YfRvn17V6OxIAioqKjAxo0bMW3aNFgsFqxduxZ9+/ZV+ltokhh35TD2ymHslcdroBzGXjmRFPtmOYe1qKgITz/9NCZPnoz169fj7NmzmDp1Kvbu3YvZs2fj9OnTHrvkdDodtm/fDqPRiE2bNoXVBWxKGHflMPbKYeyVx2ugHMZeOZEW+2aZsJaUlODSpUsYNmwY4uLiAAC///3vcf/998NqteLPf/4z8vPzXUeQbdiwAdu2bcMnn3yCrl27Krn0Jo1xVw5jrxzGXnm8Bsph7JUTabFvlgmrWq2GwWDAuXPnAEgjHABg6tSpyMjIwOHDh7F161bX/YcMGYLPPvss7J5tNDWMu3IYe+Uw9srjNVAOY6+cSIt9s+1hnTJlCs6ePYv169ejZcuWcDgcUKvVAID7778f+fn5+PzzzyGKouvZB107xl05jL1yGHvl8Rooh7FXTiTFvllUWEtLS1FUVASr1eq6bdGiRSgrK8NDDz2E8vJy1wUEgBEjRsDpdKKysjLsL2A4Y9yVw9grh7FXHq+Bchh75UR67CM+YT127Bjuu+8+3Hnnnbjhhhvw9ttvo7y8HC1btsQ///lPnDp1CuPHj8fRo0dhs9kAAPv374fJZIIoNsvic1Aw7sph7JXD2CuP10A5jL1ymkPsI7ol4Pjx47jjjjswefJkDBgwAAcPHsTf/vY3fPLJJxg0aBAA4MiRI3j44YdRVlaG2NhYtG7dGrt378amTZvQs2dPhb+DpolxVw5jrxzGXnm8Bsph7JXTXGIfsQmr1WrFtNn9TIgAAAarSURBVGnT0KlTJ7z66quu28ePH4+kpCQsXrzYo2dj+fLlOHfuHPR6PcaNG4f09HSllt6kMe7KYeyVw9grj9dAOYy9cppT7DVKLyBUqqqqUFRUhLFjxwKAq9G4Q4cOyM/PByCdqSvf/vDDDyu53IjBuCuHsVcOY688XgPlMPbKaU6xj9ge1oSEBCxbtgw33XQTAMDpdAIAkpKSPJqO1Wo1CgsLXW83lV6OcMW4K4exVw5jrzxeA+Uw9sppTrGP2IQVADp16gRAuoBarRYAUFlZ6XHRFixYgAULFqCiogIAmsROuXDHuCuHsVcOY688XgPlMPbKaS6xj9iWAHfuR48BcD3r+NOf/oQFCxZgx44d0Ol0SiwtojHuymHslcPYK4/XQDmMvXIiPfbq2bNnv6T0IhqD0+mEIAjYs2cPRFHEyZMnsWDBAnz55Zfo06eP0suLWIy7chh75TD2yuM1UA5jr5xIjn2zqLACNc88VCoVVqxYAbPZjM2bN4ftEWSRgnFXDmOvHMZeebwGymHslRPJsY/oHlZfhg8fDgD47LPP0K9fP4VX03ww7sph7JXD2CuP10A5jL1yIjH2ETuHtS5lZWUwGo1KL6PZYdyVw9grh7FXHq+Bchh75URa7JtlwkpERERETUezawkgIiIioqaFCSsRERERhTUmrEREREQU1piwEhEREVFYY8JKRERERGGNCSsRERERhTUmrEREIbRixQpYLBbXv8TERHTt2hXjxo3DP/7xD5SUlFzV5z1y5AheeeUVnDlzJsgrJiIKP83maFYiIiXNnj0baWlpqKqqQkFBAb7++mvMmTMHixcvxsqVK9GzZ88Gfb6jR49i/vz5GDJkCNq3bx+iVRMRhQcmrEREjWDEiBHo37+/6+3f/va32LFjByZPnowpU6Zg7969MBgMCq6QiCh8sSWAiEghw4YNw6xZs5CTk4MPP/wQAHD48GE88cQT6Nu3LxITE9GxY0dMmzYNubm5ro9bsWIFpk2bBgC46667XO0GK1ascN1n//79mDRpElJSUpCUlIThw4dj8+bNjfsNEhEFCRNWIiIFTZo0CQCwdetWAMC2bduQmZmJiRMn4tVXX8XUqVPxxRdf4K677oLNZgMA3HTTTXjkkUcAAL/73e/w1ltv4a233sJNN90EAPj6668xevRoFBQUYNasWfjjH/+IqKgoTJkyBevXr1fguyQiujZCUVGRqPQiiIgi1YoVK/DUU0/hiy++8GgJcJeSkoLU1FR89dVXKC8vR3R0tMf7v/nmG9x+++1YtmwZJk6cCAD43//+h2nTpmHDhg0YOnSo676iKOLGG29EUlIS1q1bB5VKqks4nU7cdtttuHjxIg4cOBCi75aIKDRYYSUiUlhMTAxKS0sBwCNZLS0txeXLl9G5c2fExsYGlGgeOnTIVaG1Wq24dOkSLl26BKvVipEjRyI7Oxtnz54N2fdCRBQK3HRFRKSw0tJStGrVCgBQVFSEl156CevWrYPVavW435UrV+r9XKdOnQIATJ8+HdOnT/d5n8LCQqSkpFzjqomIGg8TViIiBZ07dw7FxcXo0KEDAOChhx7C7t278fTTT6N3794wmUwQBAEPPfQQnE5nvZ9Pvs9LL72Evn37+rxPp06dgvcNEBE1AiasREQKWrVqFQBg+PDhKCoqwtatWzF79mzMnj3bdR+73Y6ioiKPjxMEwefnS0tLAyC1Gdx8882hWTQRUSNjDysRkUJ27NiB1157De3bt8fEiRNdG6RE0XMv7JIlS7yqq3Kva+1Etm/fvujYsSPefPNNny0EhYWFwfwWiIgaBSusRESNYMuWLcjKykJ1dTUuXryIr776Ctu2bUNycjJWrlwJvV4PvV6PIUOGYOHChaiqqkJycjK++eYb7N69Gy1atPD4fH369IFKpcLf/vY3XLlyBQaDAddffz1SU1OxaNEijB8/HgMHDsR9992HlJQUXLhwAd999x1ycnKwZ88ehaJARHR1mLASETWCv/zlLwCAqKgoxMXFoXv37njllVdw3333wWQyue63fPlyzJ49G//6179QXV2NwYMHY/369Rg7dqzH52vdujX+/ve/4+9//zueeeYZOBwOLF68GKmpqRg0aBC2bNmCV199Fe+++y6Ki4sRHx+Pnj17Ys6cOY36fRMRBQPnsBIRERFRWGMPKxERERGFNSasRERERBTWmLASERERUVhjwkpEREREYY0JKxERERGFNSasRERERBTWmLASERERUVhjwkpEREREYY0JKxERERGFtf8Pm1J+KWXtOYwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制条形图\n",
    "\n",
    "plt.figure(figsize = (10,6))\n",
    "plt.plot(true_data['date'],true_data['actual'],'b-',linewidth = 0.8,label = 'actual')\n",
    "plt.plot(test_temp['test_date'],test_temp['pred_temp'],'ro',label = 'predictions')\n",
    "plt.legend(loc = 'best')\n",
    "\n",
    "plt.xticks(rotation = 45)\n",
    "plt.xlabel('Date');plt.ylabel('Temperature');plt.title('Temp changes with date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可以看出，模型预测的温度与真实数据基本上接近。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**关键问题**\n",
    "\n",
    " * 样本数的增加，会对模型效果产生什么影响？\n",
    " * 特征数量的增加，会对模型效果产生什么影响？\n",
    " * 对模型的运算效率会有何影响？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 数据与特征对结果影响分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>ws_1</th>\n",
       "      <th>prcp_1</th>\n",
       "      <th>snwd_1</th>\n",
       "      <th>temp_2</th>\n",
       "      <th>temp_1</th>\n",
       "      <th>average</th>\n",
       "      <th>actual</th>\n",
       "      <th>friend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sat</td>\n",
       "      <td>4.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>37</td>\n",
       "      <td>45.6</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Sun</td>\n",
       "      <td>5.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>40</td>\n",
       "      <td>45.7</td>\n",
       "      <td>39</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mon</td>\n",
       "      <td>6.26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>45.8</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Tues</td>\n",
       "      <td>5.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>45.9</td>\n",
       "      <td>38</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Wed</td>\n",
       "      <td>3.80</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>38</td>\n",
       "      <td>46.0</td>\n",
       "      <td>45</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day weekday  ws_1  prcp_1  snwd_1  temp_2  temp_1  average  \\\n",
       "0  2011      1    1     Sat  4.92    0.00       0      36      37     45.6   \n",
       "1  2011      1    2     Sun  5.37    0.00       0      37      40     45.7   \n",
       "2  2011      1    3     Mon  6.26    0.00       0      40      39     45.8   \n",
       "3  2011      1    4    Tues  5.59    0.00       0      39      42     45.9   \n",
       "4  2011      1    5     Wed  3.80    0.03       0      42      38     46.0   \n",
       "\n",
       "   actual  friend  \n",
       "0      40      40  \n",
       "1      39      50  \n",
       "2      42      42  \n",
       "3      38      59  \n",
       "4      45      39  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('D:\\\\Py_dataset\\\\temps_extended.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2191, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**新增特征**\n",
    " * ws_1：前一天的风速\n",
    " * prcp_1：前一天的降水\n",
    " * snwd_1：前一天的降雪厚度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.数据切分**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[datetime.datetime(2011, 1, 1, 0, 0), datetime.datetime(2011, 1, 2, 0, 0), datetime.datetime(2011, 1, 3, 0, 0), datetime.datetime(2011, 1, 4, 0, 0), datetime.datetime(2011, 1, 5, 0, 0)]\n"
     ]
    }
   ],
   "source": [
    "#时间数据的标准化处理\n",
    "import datetime\n",
    "\n",
    "years = df['year']\n",
    "months = df['month']\n",
    "days = df['day']\n",
    "\n",
    "dates = [str(int(year)) +'-'+ str(int(month)) +'-'+ str(int(day)) for year,month,day in zip(years,months,days)]\n",
    "dates = [datetime.datetime.strptime(date,'%Y-%m-%d') for date in dates]\n",
    "\n",
    "print(dates[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.datetime(2011, 1, 1, 0, 0),\n",
       " datetime.datetime(2011, 1, 2, 0, 0),\n",
       " datetime.datetime(2011, 1, 3, 0, 0),\n",
       " datetime.datetime(2011, 1, 4, 0, 0),\n",
       " datetime.datetime(2011, 1, 5, 0, 0)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据切分\n",
    "\n",
    "labels = df['actual']\n",
    "features = df.drop('actual',axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\taon1\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_converter.py:129: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#观察特征的分布\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "fig,((ax1,ax2),(ax3,ax4)) = plt.subplots(ncols = 2,nrows = 2,figsize = (15,10))\n",
    "fig.autofmt_xdate(rotation = 45)\n",
    "\n",
    "#平均最高气温\n",
    "ax1.plot(dates,features['average'])\n",
    "ax1.set_xlabel('');ax1.set_ylabel('Temp');ax1.set_title('Max Average Temp')\n",
    "\n",
    "#前一天风速\n",
    "ax2.plot(dates,features['ws_1'],'r-')\n",
    "ax2.set_xlabel('');ax2.set_ylabel('WS_1');ax2.set_title('The Prior Day WS')\n",
    "\n",
    "#前一天降水\n",
    "ax3.plot(dates,features['prcp_1'],'r-')\n",
    "ax3.set_xlabel('');ax3.set_ylabel('PRCP_1');ax3.set_title('The Prior Day PRCP')\n",
    "\n",
    "#前一天的降雪量\n",
    "ax4.plot(dates,features['snwd_1'],'ro')\n",
    "ax4.set_xlabel('');ax4.set_ylabel('SNWD_1');ax4.set_title('The Prior Day SNWD')\n",
    "\n",
    "plt.tight_layout(pad=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.特征工程 - 增加新特征**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\taon1\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "# 创建一个新特征\n",
    "seasons = []\n",
    "\n",
    "for month in features['month']:\n",
    "    if month in [12,1,2]:\n",
    "        seasons.append('winter')\n",
    "    elif month in [3,4,5]:\n",
    "        seasons.append('spring')\n",
    "    elif month in [6,7,8]:\n",
    "        seasons.append('summer')\n",
    "    elif month in [9,10,11]:\n",
    "        seasons.append('autumn')\n",
    "    \n",
    "reduced_features = features[['temp_1','prcp_1','average','snwd_1']]\n",
    "reduced_features['season'] = seasons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\taon1\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:487: RuntimeWarning: invalid value encountered in true_divide\n",
      "  binned = fast_linbin(X, a, b, gridsize) / (delta * nobs)\n",
      "C:\\Users\\taon1\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:34: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  FAC1 = 2*(np.pi*bw/RANGE)**2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2730b1caba8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 799.475x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#观察reduced_features数据集上两两特征之间的关系\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(style = 'ticks',color_codes = True)\n",
    "\n",
    "#创建你喜欢的调色板\n",
    "palette = sns.xkcd_palette(['dark blue','dark green','gold','orange'])\n",
    "#绘制pairplot\n",
    "\n",
    "sns.pairplot(reduced_features,hue = 'season',diag_kind = 'kde',palette = palette,plot_kws = dict(alpha = 0.7),diag_kws = dict(shade = True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_list = list(features.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = pd.get_dummies(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.观察样本数量的增加最模型效果的影响**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集特征: (1643, 17)\n",
      "训练集标签: (1643,)\n",
      "测试集特征: (548, 17)\n",
      "测试集标签: (548,)\n"
     ]
    }
   ],
   "source": [
    "#对数据集进行切分\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_features,test_features,train_labels,test_labels = train_test_split(features,labels,test_size = 0.25,random_state = 0)\n",
    "\n",
    "\n",
    "print('训练集特征:',train_features.shape)\n",
    "print('训练集标签:',train_labels.shape)\n",
    "print('测试集特征:',test_features.shape)\n",
    "print('测试集标签:',test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#读取原始数据集\n",
    "original_df = pd.read_csv('D:\\\\Py_dataset\\\\temps.csv')\n",
    "\n",
    "original_labels = original_df['actual']\n",
    "original_features = original_df.drop('actual',axis = 1)\n",
    "original_feature_list = list(original_features)\n",
    "original_features = pd.get_dummies(original_features)\n",
    "\n",
    "\n",
    "\n",
    "#切分数据集\n",
    "original_train_features,original_test_features,original_train_labels,original_test_labels = train_test_split(original_features,\n",
    "                                                                                                             original_labels,\n",
    "                                                                                                             test_size = 0.25,\n",
    "                                                                                                             random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#创建一个新的多样本数据集\n",
    "\n",
    "train_features_1 = train_features.drop(['ws_1','prcp_1','snwd_1'],axis=1)\n",
    "train_labels_1 = train_labels\n",
    "test_features_1 = test_features.drop(['ws_1','prcp_1','snwd_1'],axis=1)\n",
    "test_labels_1 = test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "小样本训练集下的均方误差: 5.83\n"
     ]
    }
   ],
   "source": [
    "#建立同样的树模型,学习不同大小的训练集，测试相同的测试集\n",
    "original_feature_list = list(original_features.columns)\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "RF = RandomForestRegressor(n_estimators = 100,random_state = 0)\n",
    "RF.fit(original_train_features,original_train_labels)\n",
    "\n",
    "original_predictions = RF.predict(test_features_1)\n",
    "\n",
    "#使用mse指标对测试结果进行评估                                \n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "MSE = mean_squared_error(original_predictions,test_labels_1)\n",
    "RMSE = round(np.sqrt(MSE),2)\n",
    "\n",
    "print('小样本训练集下的均方误差:',RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大样本训练集下的均方误差: 5.35\n"
     ]
    }
   ],
   "source": [
    "#建立相同的模型，学习更大样本的数据集\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "RF = RandomForestRegressor(n_estimators = 100,random_state = 0)\n",
    "RF.fit(train_features_1,train_labels_1)\n",
    "\n",
    "predictions = RF.predict(test_features_1)\n",
    "\n",
    "#使用mse指标对测试结果进行评估                                \n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "MSE = mean_squared_error(predictions,test_labels_1)\n",
    "RMSE = round(np.sqrt(MSE),2)\n",
    "\n",
    "print('大样本训练集下的均方误差:',RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "样本数量增加，模型提神幅度:9 %\n"
     ]
    }
   ],
   "source": [
    "print('样本数量增加，模型提神幅度:{}'.format(round(100*(5.83-5.35)/5.35),2),'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4.观察特征数量的增加，对结果的影响**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>ws_1</th>\n",
       "      <th>prcp_1</th>\n",
       "      <th>snwd_1</th>\n",
       "      <th>temp_2</th>\n",
       "      <th>temp_1</th>\n",
       "      <th>average</th>\n",
       "      <th>friend</th>\n",
       "      <th>weekday_Fri</th>\n",
       "      <th>weekday_Mon</th>\n",
       "      <th>weekday_Sat</th>\n",
       "      <th>weekday_Sun</th>\n",
       "      <th>weekday_Thurs</th>\n",
       "      <th>weekday_Tues</th>\n",
       "      <th>weekday_Wed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1717</th>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>7.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>62</td>\n",
       "      <td>71.0</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1732</th>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>71</td>\n",
       "      <td>65.7</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>2011</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>59</td>\n",
       "      <td>67.6</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>15.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>54</td>\n",
       "      <td>51.9</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1783</th>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>9.40</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>49.8</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year  month  day   ws_1  prcp_1  snwd_1  temp_2  temp_1  average  \\\n",
       "1717  2015      9   15   7.61    0.00       0      69      62     71.0   \n",
       "1732  2015      9   30   4.25    0.00       0      70      71     65.7   \n",
       "152   2011      6    2   5.82    0.17       0      61      59     67.6   \n",
       "427   2012      3    4  15.66    0.00       0      44      54     51.9   \n",
       "1783  2015     11   20   9.40    0.08       0      48      48     49.8   \n",
       "\n",
       "      friend  weekday_Fri  weekday_Mon  weekday_Sat  weekday_Sun  \\\n",
       "1717      53            0            0            0            0   \n",
       "1732      71            0            0            0            0   \n",
       "152       59            0            0            0            0   \n",
       "427       56            0            0            0            1   \n",
       "1783      41            1            0            0            0   \n",
       "\n",
       "      weekday_Thurs  weekday_Tues  weekday_Wed  \n",
       "1717              0             1            0  \n",
       "1732              0             0            1  \n",
       "152               1             0            0  \n",
       "427               0             0            0  \n",
       "1783              0             0            0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_features.iloc[:5,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exp模型的误差: 5.193822165431869\n"
     ]
    }
   ],
   "source": [
    "#创建RF模型\n",
    "RF_exp = RandomForestRegressor(n_estimators = 100,random_state = 0)\n",
    "RF_exp.fit(train_features,train_labels)\n",
    "exp_predictions = RF_exp.predict(test_features)\n",
    "MSE = mean_squared_error(exp_predictions,test_labels)\n",
    "RMSE = np.sqrt(MSE)\n",
    "print('exp模型的误差:',RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "特征增加后，模型的提升程度3.0\n"
     ]
    }
   ],
   "source": [
    "print('特征增加后，模型的提升程度{}'.format(100*round((5.35-5.19)/5.35,2),'%'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5.继续研究特征重要性这个指标**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable:temp_2               Importance :0.85\n",
      "Variable:temp_1               Importance :0.05\n",
      "Variable:weekday              Importance :0.02\n",
      "Variable:average              Importance :0.02\n",
      "Variable:year                 Importance :0.01\n",
      "Variable:month                Importance :0.01\n",
      "Variable:day                  Importance :0.01\n",
      "Variable:ws_1                 Importance :0.01\n",
      "Variable:snwd_1               Importance :0.01\n",
      "Variable:prcp_1               Importance :0.0\n",
      "Variable:friend               Importance :0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None, None, None, None, None, None, None]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances = list(RF_exp.feature_importances_)\n",
    "#将特征名字和数值组合在一起\n",
    "feature_importances = [(feature,round(importance,2)) for feature,importance in zip(feature_list,importances)]\n",
    "feature_importances = sorted(feature_importances,key=lambda x:x[1],reverse = True)\n",
    "\n",
    "[print('Variable:{:20} Importance :{}'.format(*pair)) for pair in feature_importances]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从以上结果可以看出，对结果影响最大的两个特征是temp_2，temp_1。ws_1对结果的影响较小。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Feature Impotances')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#特征重要性程度可视乎\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "x_vars = list(range(len(importances)))\n",
    "plt.bar(x_vars,importances,orientation = 'vertical',color = 'r',edgecolor = 'b',linewidth = 1.2)\n",
    "plt.xticks(x_vars,feature_list,rotation = 90)\n",
    "plt.xlabel('Feature');plt.ylabel('Importance');plt.title('Feature Impotances')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('temp_2', 0.85),\n",
       " ('temp_1', 0.05),\n",
       " ('weekday', 0.02),\n",
       " ('average', 0.02),\n",
       " ('year', 0.01)]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Cumsum Feature Importances')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#提取已排好序的特征和数值\n",
    "\n",
    "sorted_importances = [importance[1] for importance in feature_importances]\n",
    "sorted_features = [importance[0] for importance in feature_importances]\n",
    "\n",
    "x_vars = range(11)\n",
    "#累计特征的重要性\n",
    "cumulative_importance = np.cumsum(sorted_importances)\n",
    "#绘制折线图\n",
    "plt.plot(x_vars,cumulative_importance,'g-')\n",
    "#绘制阈值线\n",
    "plt.hlines(y = 0.95,xmin = 0,xmax = len(sorted_features),color = 'r',linestyle = 'dashed',label = '95%')\n",
    "plt.legend(loc = 'best')\n",
    "#x轴\n",
    "plt.xticks(x_vars,sorted_features,rotation = 90)\n",
    "#标注\n",
    "plt.xlabel('Feature');plt.ylabel('Importance');plt.title('Cumsum Feature Importances')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由图可见，前五个的特征累加的重要性和达到了95%。接下来选取前五个特征作为数据集的特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Important train features shape: (1643, 5)\n",
      "Important test features shape: (548, 5)\n",
      "reduced模型精度: 5.245160734561607\n"
     ]
    }
   ],
   "source": [
    "#选择这些特征\n",
    "important_feature_names = [feature[0] for feature in feature_importances[:5]]\n",
    "important_indices = [feature_list.index(feature) for feature in important_feature_names]\n",
    "\n",
    "#重新创建训练集\n",
    "important_train_features = train_features.iloc[:,important_indices]\n",
    "important_test_features = test_features.iloc[:,important_indices]\n",
    "#数据维度\n",
    "print('Important train features shape:',important_train_features.shape)\n",
    "print('Important test features shape:',important_test_features.shape)\n",
    "\n",
    "#再次训练模型\n",
    "exp_RF = RandomForestRegressor(n_estimators = 100,random_state = 0)\n",
    "exp_RF.fit(important_train_features,train_labels)\n",
    "reduced_predictions = exp_RF.predict(important_test_features)\n",
    "MSE = mean_squared_error(test_labels,reduced_predictions)\n",
    "RMSE = np.sqrt(MSE)\n",
    "print('reduced模型精度:',RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>original data</td>\n",
       "      <td>5.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>full data</td>\n",
       "      <td>5.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>reduced features</td>\n",
       "      <td>5.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Model  RMSE\n",
       "0     original data  5.83\n",
       "1         full data  5.19\n",
       "2  reduced features  5.24"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_result = pd.DataFrame(data = {'Model':['original data','full data','reduced features'],'RMSE':[5.83,5.19,5.24]})\n",
    "model_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对比三种模型，可以看出在全样本数据集下，模型的精度最高。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.模型调参"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**31.随机参数选择**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "#建立树的个数\n",
    "n_estimators = [int(x) for x in np.linspace(start = 200,stop = 2000,num = 10)]\n",
    "\n",
    "#最大特征的选择方式\n",
    "max_features = ['auto','sqrt']\n",
    "\n",
    "#树的最大深度\n",
    "max_depth = [int(x) for x in np.linspace(10,20,num =2)]\n",
    "max_depth.append(None)\n",
    "\n",
    "#节点可分裂所需要的最小样本数\n",
    "min_samples_split = [2,5,10]\n",
    "\n",
    "#叶子结点最小样本数\n",
    "min_samples_leaf = [1,2,4]\n",
    "\n",
    "#样本采样方法\n",
    "bootstrap = [True,False]\n",
    "\n",
    "#随机参数空间\n",
    "random_grid = {'n_estimators':n_estimators,'max_features':max_features,'max_depth':max_depth,'min_samples_leaf':min_samples_leaf,\n",
    "              'min_samples_split':min_samples_split,'bootstrap':bootstrap}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   55.7s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  3.8min\n",
      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:  7.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score='raise-deprecating',\n",
       "          estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid='warn', n_iter=100, n_jobs=-1,\n",
       "          param_distributions={'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000], 'max_features': ['auto', 'sqrt'], 'max_depth': [10, 20, None], 'min_samples_leaf': [1, 2, 4], 'min_samples_split': [2, 5, 10], 'bootstrap': [True, False]},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn', scoring='neg_mean_squared_error',\n",
       "          verbose=2)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF = RandomForestRegressor()\n",
    "RF_random = RandomizedSearchCV(RF,param_distributions = random_grid,n_iter=100,scoring = 'neg_mean_squared_error',\n",
    "                              cv = 3,verbose=2,random_state = 42,n_jobs = -1)\n",
    "\n",
    "RF_random.fit(train_features,train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 1800,\n",
       " 'min_samples_split': 10,\n",
       " 'min_samples_leaf': 4,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': None,\n",
       " 'bootstrap': True}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_evaluation(model,train_set,train_label,test_set,test_label):\n",
    "    model.fit(train_set,train_label)\n",
    "    predictions = model.predict(test_set)\n",
    "    MSE = mean_squared_error(predictions,test_label)\n",
    "    RMSE = np.sqrt(MSE)\n",
    "    return RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "RF = RandomForestRegressor(n_estimators=1800,min_samples_split=10,min_samples_leaf=4,max_features='auto',random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型效果: 5.111886820330025\n"
     ]
    }
   ],
   "source": [
    "print('模型效果:',model_evaluation(RF,train_features,train_labels,test_features,test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random_RF模型效果提升:1.5414258188824674 %\n"
     ]
    }
   ],
   "source": [
    "print('Random_RF模型效果提升:{}'.format(100*(5.19-5.11)/5.19),'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "随机参数空间搜索帮助我们确定了大致的参数范围，接下来我们将进一步调节参数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.2.网格参数搜索**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#创建参数空间\n",
    "param_grid = {'n_estimators':[1600,1700,1800,1900,2000],\n",
    "             'min_samples_split':[8,9,10,11,12],\n",
    "             'min_samples_leaf':[3,4,5,6]}\n",
    "\n",
    "RF = RandomForestRegressor()\n",
    "Grid_RF = GridSearchCV(RF,param_grid = param_grid,scoring = 'neg_mean_squared_error',n_jobs = -1,cv=3,verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  6.0min\n",
      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed: 11.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'n_estimators': [1600, 1700, 1800, 1900, 2000], 'min_samples_split': [8, 9, 10, 11, 12], 'min_samples_leaf': [3, 4, 5, 6]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_mean_squared_error', verbose=2)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Grid_RF.fit(train_features,train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_leaf': 6, 'min_samples_split': 9, 'n_estimators': 1700}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Grid_RF.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_RF = RandomForestRegressor(n_estimators = 1700,min_samples_leaf = 6,min_samples_split = 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型效果: 5.110432321187415\n"
     ]
    }
   ],
   "source": [
    "print('模型效果:',model_evaluation(grid_RF,train_features,train_labels,test_features,test_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从模型的效果来看，这次的参数调优较上次优化幅度不大，可以确定最优参数的数值了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>original data</td>\n",
       "      <td>5.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>full data</td>\n",
       "      <td>5.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>reduced features</td>\n",
       "      <td>5.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>opt model</td>\n",
       "      <td>5.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Model  RMSE\n",
       "0     original data  5.83\n",
       "1         full data  5.19\n",
       "2  reduced features  5.24\n",
       "3         opt model  5.11"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_result = pd.DataFrame(data = {'Model':['original data','full data','reduced features','opt model'],'RMSE':[5.83,5.19,5.24,5.11]})\n",
    "model_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_varis = list(model_result['Model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_values = list(model_result['RMSE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Different model result')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (6,4))\n",
    "plt.bar(range(len(model_varis)),model_values,color = 'r',edgecolor = 'b',orientation = 'vertical')\n",
    "plt.xticks(range(len(model_varis)),model_varis,rotation = 0)\n",
    "plt.xlabel('Model');plt.ylabel('RMSE');plt.title('Different model result')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.项目总结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(1) 该项目由三部分构成，数据预处理，RF模型建立，参数调优。\n",
    "\n",
    "(2) 该数据集数据比较整齐，所需要处理的部分不多。在此基础上，我们对数据做了特征工程，基于月份数据，新创建季节特征。\n",
    "\n",
    "(3) 对比了不同样本量，不同特征量对最终结果的影响，可以确定更多的数据量，更多的样本特征，对于建立更好的模型是有帮助作用的。\n",
    "\n",
    "(4) 特征选择，模型的RMSE值基本没有变化，模型的运算效率大大提升。具体情况需结合实际任务而确定。\n",
    "\n",
    "(5) 参数调优，介绍了RandomizedSearchCV和GridSearchCV两种方法，两种方法可以结合使用，以确定模型的最优参数。\n",
    "\n",
    "(6) 学会查阅参考文档，对于不懂的内容，学习，模仿，练习。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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